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DYNAMIC ANALYSIS OF AMBIENT AIR POLLUTION AND ASSESSMENT OF EFFECTS OF CLIMATE CHANGE ON AIR
QUALITY IN URBAN AREAS OF PAKISTAN
By
ANJUM RASHEED
DEPARTMENT OF ENVIRONMENTAL SCIENCES
FATIMA JINNAH WOMEN UNIVERSITY RAWALPINDI, PAKISTAN
APRIL, 2014
DYNAMIC ANALYSIS OF AMBIENT AIR POLLUTION AND ASSESSMENT OF EFFECTS OF CLIMATE CHANGE ON AIR
QUALITY IN URBAN AREAS OF PAKISTAN
A dissertation presented
by
Anjum Rasheed
to
The Department of Environmental Sciences
In partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
in the subject of
Environmental Sciences
Fatima Jinnah Women University,
Rawalpindi, Pakistan
April, 2014
Declaration
I, hereby, declare that the work presented in this thesis is my own effort based on
actual experimentation except where otherwise acknowledged and that the thesis is
my own composition. No part of the thesis has previously been presented for any
degree.
(Anjum Rasheed)
Certificate This Thesis submitted by Anjum Rasheed is accepted in its present form by the
Department of Environmental Sciences, Fatima Jinnah Women University,
Rawalpindi, as satisfying the thesis requirement for the Degree of Doctor of
Philosophy in Environmental Sciences.
Supervisor: ______________
Dr. Uzaira Rafique
To my Beloved Mentors and Teachers
with Deepest Gratitude
for their
Affection and Kindness
i
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to all the people who provided
any sort of support in completion of my Ph.D. thesis. First of all, I would like to
acknowledge Higher Education Commission (HEC) of Pakistan for awarding me
Indigenous scholarship to do my Ph.D. and for providing me financial support to do my
research work at NCSU. I am thankful to Mr. Baqir Husnain, Project Manager, Indigenous
Ph.D. Fellowship Program and Mr. Jehanzeb Khan Project Director, International
Research Support Initiative Program (IRSIP) at HEC for being very facilitating. I thank
Dr. Samina Amin Qadir, Vice Chancellor, FJWU for always being very affectionate and
kind to facilitate me during my Ph.D. I acknowledge Dr. Walter Robinson, Head,
Department of Marine, Earth and Atmospheric Sciences, NCSU for providing academic
support and facilities at NCSU. I am extremely grateful to my supervisor Dr. Uzaira
Rafique for all her support throughout my Ph.D. She has been always understanding and
facilitating during my studies. I am highly grateful to my co-supervisor, Dr. Viney P.
Aneja, Professor, Department of Marine, Earth and Atmospheric Sciences, North Carolina
State University (NCSU), USA for his research inputs and valuable guidance to conduct
my research work. I am thankful to him for making himself available anytime despite his
busy schedule. It had been a great opportunity for me to learn from his expertise in the
field of air quality. My special thanks to him and Mrs. Poonam Aneja, for their kindness
and hospitality. I thank Dr. Anantha Aiyyer, Associate Professor, MEAS, NCSU for his
guidance and contribution to my research work. He is acknowledged for his help during
compilation of Weather Research and Forecasting (WRF) model. I am grateful to Mr.
Burhan Ahmed, Meteorologist, Pakistan Meteorological Department (PMD) for providing
me training on WRF model and GrADS and enabling me to complete my research work. I
would like to thank Mr. Qamar and Mr. Sajjad, PMD for helping me in installations of
WRF model and for guiding me in interpretation of weather patterns.
I am thankful to Mr. Asif S. Khan, Director General, Pakistan Environmental
Protection Agency for his kindness and for providing me air quality data of Pakistan. I am
extremely grateful to Mr. Zia Ul Islam, Director, Climate Change Division for his constant
and unconditional support during my Ph.D. I offer profound gratitude to him for giving
ii
me confidence and for inspiring me to proceed for higher studies. I am highly obliged to
Mr. Daisaku Kiyota, CTII, Japan for making me believe in myself and helping me to
tackle the difficult situations and people. I am thankful to Mr. Toshiharu Ochi, Green Blue
Ltd., Japan for his guidance, research input and valuable time. My sincere gratitude go to
all the JICA Experts Mr. Takashi Onuma, Mr. Kageyam, Mr. Kenichi Kuramoto, Mr.
Fujimura, Mr. Takahisa Sato, Mr. Akimoto and Mr. Hosono for their kindness and
cooperation during my Ph.D.
I am thankful to my teachers Dr. Uzaira Rafique, Dr. Shazia Iftikhar, Dr. Asma
Jabeen, Ms. Fareena Iqbal, Dr. Sheikh Saeed, Dr. Rohama Gill, Dr. Azra Yasmin, Dr.
Sofia Khalid, Dr. Abida Farooqi and Dr. Naeema for their guidance and support during
my studies at FJWU. I am extremely grateful to my teachers Mrs. Razia Mukhtar Naqvi,
Ms. Halima, Ms. Riffat Naqvi, Ms. Fiza, Ms. Adeeba, Ms. Waqar-un-Nisa, Ms.
Farkhanda, Ms. Maimoona, Ms. Shahida, Ms. Azra Jillani, Ms. Kanwal Jamshaid, Dr.
Uzaira Rafique and Dr. Viney P. Aneja for being wonderful and kind teachers. I am
thankful to Ms. Priya Pillai, and Mr. Praju for extending all their support during my stay at
NCSU and for being always very helpful. I also thank Ms. Khairun Nisa, NCSU for her
guidance during compilation of WRF model. I am grateful to Ms. Uzma, Ms. Sumreen,
Ms. Farah, Ms. Saima and Ms. Noshabah for their support and cooperation during my
studies.
I highly regard the love and care of my grandfather who had always wished for my
higher studies and a prosperous career; however, I regret not to share the happiness of
completion of my Ph.D. due to his death a day before the convocation. I am thankful to
my parents for their love and for providing me opportunity for higher education. My
special thanks to my father for always standing by my side and for supporting me
throughout my life. I am extremely grateful to my aunt, Mrs. Kausar Jabbar, for her love
and affection and for helping me in my studies. I am indebted to my brother Sohail
Rasheed for his care, kindness and financial support during my stay in US. I thank my
grandparents, Uncle Mushtaq, Uncle Khalid, Uncle Tariq, Uncle Asif and Ms. Shahida for
all their support and guidance during my studies. I thank my siblings Waqas, Saadia,
Maria and Maryam for giving me space to do my Ph.D. thesis.
I am thankful to Allah Almighty for enabling me to complete my Ph.D. and for providing me wonderful opportunities in my life.
iii
ABSTRACT
Air pollution is becoming a major environmental issue in Pakistan owing to rapid
urbanization and economic growth. In order to assess the extent of air quality within the
major urban environments, PM2.5 pollutant has been analyzed during the period 2007-2011
in Islamabad; and 2007 to 2008 in Lahore, Peshawar and Quetta. Seasonal and diurnal
variation of PM2.5 mass concentration and meteorological factors affecting the emissions,
secondary PM2.5 formation and accumulation of pollutants have been analyzed. Air quality
monitoring data and meteorological data were obtained from Federal and Provincial
Pakistan Environmental Protection Agencies. Ambient air quality data of Islamabad,
Pakistan, for six representative air pollutants (carbon monoxide (CO), oxides of nitrogen
(NO and NOy′), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), non-
methane hydrocarbons (NMHCs), and meteorology was collected for five years (2007-
2011).
In Islamabad, the annual average PM2.5 mass concentrations were 81.1±48.4 µg m-
3, 93.0±49.9 µg m-3, 47.8±33.2 µg m-3, 79.0±49.2 µg m-3, 66.1±52.1 µg m-3 during 2007 to
2011, respectively; and the highest hourly values observed were 303 µg m-3 during
December 2007, 495.0 µg m-3 during November 2008, 259.8 µg m-3 during September
2009, 456.0 µg m-3 during October 2010, and 379.0 µg m-3 during January 2011.
Comparison of the four cities during summer 2007 to spring 2008 shows that all the four
cities have PM2.5 concentration exceeding the Pakistan National Environmental Quality
Standards (annual average concentration of 25 µg m-3; and 24 hourly average
concentration of 40 µg m-3) for ambient air. During the same time period (i.e. summer
2007 to spring 2008), the highest seasonal PM2.5 mass concentration for Islamabad was
observed as 98.5 µg m-3 during spring 2008; 150.4±87.9 µg m-3; 104.1±51.1 µg m-3 and
72.7±55.2 µg m-3 for Lahore, Peshawar, and Quetta during fall 2007 respectively. Wind
speed and temperature have a negative correlation with the mass concentration of PM2.5.
Moreover, the relation of vapor pressure is weak but mostly negative. Diurnal profile for
all the cities suggests an association of PM2.5 with vehicular traffic.
Data analysis revealed annual average mass concentration of PM2.5 (~45 to ~95 µg
m-3) and NO concentration (~41 to ~120 µg m-3) exceed the Pakistan’s National
Environmental Quality Standards (NEQS). The annual O3 concentration is within the
iv
permissible limits; however, some of the hourly concentration exceeds the NEQS mostly
during summer months. Correlation studies suggest that carbon monoxide has as a
significant (p-value ≤0.01) positive correlation with NO and NOy′; whereas, with ozone, a
significant (p-value ≤0.01) negative correlation is observed. The regression analysis
estimates the background CO concentration to be ~250 to ~500 ppbv in Islamabad. The
higher ratio of CO/NO (~10) suggests that mobile sources are the major contributor to NO
concentration. On the other hand, the ratio analysis of SO2/NO for Islamabad (~0.011)
indicates that the point sources are contributing to SO2 in the city. NO and SO2 correlation
indicates a direct emission sources containing high sulfur content. The correlation of PM2.5
and NO suggests that a fraction of secondary PM2.5 is produced by chemical conversion of
NO into nitrates. The regional background O3 concentration for Islamabad has been
determined to be ~31ppbv. The study suggests that there is an increase in O3 concentration
with increases in photochemical conversion of NO to reservoir NOy′ species.
In order to investigate the contribution of local or transboundary sources of air
pollution towards the high ozone episodes in Islamabad, backward trajectories using
NOAA HYSPLIT model were computed. Furthermore, simulations of two selected high
ozone episodes were carried out by using Weather Research and Forecasting (WRF)
model to assess the influence of meteorological conditions on level and variation of ozone
during episode period. The HYSPLIT back trajectories have revealed that a number of
back trajectories are originated from west, south-west and eastern transboundary pollution
sources. It has been observed that local sources are also contributing towards pollution in
Islamabad when high concentrations are observed during stagnant conditions.
Furthermore, when air masses from west, south-west and south-east are advecting into the
city, stagnant conditions lead to accumulation of pollutants. It has been revealed that most
of the episodes occurred during stagnant conditions followed by advection from far-off
regions.
The study recommends that an extended air quality and climate modeling may be
conducted to get an insight into the tropospheric chemistry of the area leading to many
frequent high ozone episodes. There is also need to develop effective control strategies to
meet the ambient air quality standards through the use of an integrated assessment model.
v
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................... i
ABSTRACT .................................................................................................................... iii
LIST OF FIGURES ....................................................................................................... viii
CHAPTER 1: INTRODUCTION .............................................................................. 1
1.1. Urban Air Pollution ............................................................................................ 1
1.2. Major Air Pollutants and Tropospheric Chemistry .............................................. 2
1.2.1 Sulfur Dioxide............................................................................................. 2
1.2.2 Carbon Monoxide ....................................................................................... 3
1.2.3 Methane ...................................................................................................... 4
1.2.4 Non-Methane Hydrocarbons ....................................................................... 5
1.2.5 Hydroxyl Radical ........................................................................................ 6
1.2.6 Nitrogen Oxides .......................................................................................... 7
1.2.7 Particulate Matter in the Atmosphere........................................................... 8
1.2.8 Tropospheric Ozone Formation ................................................................. 10
1.3 Air Pollution and Meteorology ......................................................................... 11
1.4 Air Pollution and Climate Change .................................................................... 14
1.5 Air Pollution and Climate Change Scenario in Pakistan .................................... 16
1.6 Climate Modeling............................................................................................. 19
1.7 Significance of the Research Work ................................................................... 20
1.8 Objectives ........................................................................................................ 21
CHAPTER 2: METHODOLOGY............................................................................. 22
2.1. Description of Sampling Sites .............................................................................. 22
2.1.1. Islamabad ...................................................................................................... 22
2.1.2. Lahore ........................................................................................................... 23
2.1.3. Peshawar ....................................................................................................... 24
2.1.4. Quetta ............................................................................................................ 25
2.2. Experimental Methods ......................................................................................... 26
2.2.1. Data Collection .............................................................................................. 26
2.2.2. Ambient Particulate Monitor.......................................................................... 29
2.2.3. NOx Analyzer: ............................................................................................... 30
2.2.3. Ambient SO2 Monitor: ................................................................................... 31
vi
2.2.4. Ambient CO Monitor: .................................................................................... 34
2.2.5. Ambient O3 Monitor ...................................................................................... 35
2.2.6. Ambient Hydrocarbon Monitor ...................................................................... 36
2.2.7. Combined Wind Vane and Anemometer ........................................................ 37
2.3. Synoptic Analysis for PM2.5 High Episodes: ..................................................... 38
2.4. Back Trajectory Modeling ................................................................................ 39
2.5. Weather Research and Forecasting (WRF) Model Simulations ......................... 39
CHAPTER 3: RESULTS AND DISCUSSION ............................................................... 40
SECTION I: ....Analysis of Fine Particulate Matter (PM2.5) in Urban Areas of Pakistan: An Observational-Based Analysis ........................................................................................ 40
3.1. Spatial and Temporal Variation of PM2.5 .............................................................. 40
3.2. Diurnal Profile of PM2.5 ....................................................................................... 44
3.3. Effect of Meteorology on PM2.5............................................................................ 51
3.5. Analysis of High PM2.5 Episodes.......................................................................... 61
3.5.1. Islamabad Winter High PM2.5 Episode (December 1-9, 2007) ........................ 62
3.5.2. Lahore High PM2.5 Episode in Winter (February 1-25, 2008) ......................... 66
3.5.3. Peshawar High Winter PM2.5 Episode (December 1-22, 2007) ....................... 69
3.5.4. Quetta High PM2.5 Winter Episode (December 1-18, 2007): .......................... 72
3.5.5. Lahore High PM2.5 Episode in Summer (June 1-12, 2007).............................. 75
3.5.6. Quetta High PM2.5 Summer Episode (August 13-19, 2007) ............................ 80
3.6. Conclusion ........................................................................................................... 83
SECTION II: Measurements and Analysis of Air Quality in Islamabad, Pakistan ........ 85
4.1 Meteorology ..................................................................................................... 85
4.2 Average Concentration of Pollutants ................................................................ 85
4.3. Correlation of Air Pollutants ................................................................................ 90
4.4. Photochemistry of Ozone Formation .................................................................. 100
4.5. Diurnal Variation of Pollutants ........................................................................... 102
4.9. Conclusions ....................................................................................................... 111
SECTION III: . Back Trajectory Analysis and Simulation of Ozone High Episodes by WRF Model in Islamabad, Pakistan ....................................................................................... 113
5.1. Ozone Episodes in Islamabad City ..................................................................... 113
5.2. Back Trajectory Analysis ................................................................................... 113
5.3. Weather Research and Forecasting (WRF) Model Simulations ........................... 137
vii
5.3.1. High Ozone Episode during June 9-15, 2009 ............................................... 137
5.3.2. High Ozone Episode during August 15-19, 2011 ......................................... 155
5.4. Conclusions ....................................................................................................... 167
References .................................................................................................................... 168
Appendices................................................................................................................... 168
Appendix A: Research Publications .............................................................................. 168
Appendix B: Conference Presentations ......................................................................... 168
viii
LIST OF FIGURES
Figure 2.1. Physical Map of Pakistan showing the sampling sites in Islamabad,
Lahore, Peshawar and Quetta .................................................................................. 22
Figure 2.2. Monitoring Site at Central Laboratory for environmental Analysis and
Networking (CLEAN), Pak-EPA…………………………………………………….23
Figure 2.3. Monitoring Site of Punjab-EPA, Lahore ............................................... 24
Figure 2.4. Monitoring Site of KP-EPA, Peshawar ................................................ 25
Figure 2.5. Monitoring Site of Balochistan-EPA, Quetta ......................................... 26
Figure 2.6. Fixed Automated Air Quality Monitoring Station at Central Laboratory of
Environmental Analysis and Networking (CLEAN), Pak-EPA ................................ 27
Figure 2.7. Analyzers for Ambient Air Installed within the Automated Air Quality
Monitoring Station .................................................................................................. 28
Figure 3.1. Annual and Seasonal Average PM2.5 Mass Concentration (µg m-3) in
Islamabad during 2007-2011 (±1 standard deviation is also shown in the figure; No.
of data points given above the bars)………………………………………………….41
Figure 3.2. Comparison of Annual and Seasonal Average PM2.5 Mass Concentration
(µg m-3) in Islamabad, Lahore, Peshawar and Quetta during Summer 2007-Spring
2008 (±1 standard deviation is also shown in the figure; No. of data points given
above the bars) ........................................................................................................ 42
Figure 3.3. Integrated Average Diurnal Profile of PM2.5 Mass Concentration (µg m-3)
in Islamabad, Lahore, Peshawar and Quetta for 2007-2011 (±1 standard deviation is
also shown in the figure) .......................................................................................... 45
Figure 3.4(a). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Islamabad ................................................................................................................ 46
Figure 3.4(b). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Lahore ..................................................................................................................... 47
ix
Figure 3.4(c). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Peshawar ................................................................................................................ .48
Figure 3.4(d). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Quetta ..............................................................................................................……49
Figure 3.5. Workday-Weekend Variation of PM2.5 Mass Concentration in (a)
Islamabad; (b) Lahore; (c) Peshawar; and (d) Quetta .............................................. 51
Figure 3.6. Effect of Temperature on PM2.5 Mass Concentration (µg m-3) during
2007-2011 in (a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ..................... 53
Figure 3.7. Effect of Solar Radiation on PM2.5 Mass Concentration (µg m-3) during
2007-2011 in (a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ..................... 55
Figure 3.8. Effect of Wind Speed on PM2.5 Mass Concentration (µg m-3) during
2007-2011 in a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ..................... 57
Figure 3.9. Effect of Vapour Pressure on PM2.5 Mass Concentration (µg m-3) during
2007-2011 in a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ...................... 59
Figure 3.10(a). Time Series of PM2.5 Mass Concentration and Temperature in
Islamabad during December 1-9, 2007 .................................................................... 62
Figure 3.10(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Islamabad
during December 1-9, 2007 ..................................................................................... 63
Figure 3.10(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)
in Islamabad during December 1-9, 2007 ................................................................ 64
Figure 3.10(d). Back Trajectory Analysis of High PM2.5 Episode in Islamabad during
December 1-9, 2007 ................................................................................................ 65
Figure 3.11(a). Time Series of PM2.5 Mass Concentration and Temperature in Lahore
during February 1-25, 2008 .................................................................................... 66
Figure 3.11(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore
during February 1-25, 2008 ..................................................................................... 67
Figure 3.11(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)
in Lahore during February 1-25, 2008 ..................................................................... 67
x
Figure 3.11 (d). Back Trajectory Analysis of High PM2.5 in Lahore during February
1-25, 2008…………………………………………………………………………….68
Figure 3.12(a). Time Series of PM2.5 Mass Concentration and Temperature in
Peshawar during December 1-22, 2007.................................................................... 69
Figure 3.12(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Peshawar
during December 1-22, 2007 .................................................................................. 70
Figure 3.12(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)
in Peshawar during December 1-22, 2007 ................................................................ 70
Figure 3.12(d). Back Trajectory Analysis of High PM2.5 in Peshawar during
December 1-22, 2007 .............................................................................................. 71
Figure 3.13(a). Time Series of PM2.5 and Temperature in Quetta during December 1-
18, 2007 .................................................................................................................. 72
Figure 3.13(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta
during December 1-18, 2007 ................................................................................... 73
Figure 3.13(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)
in Quetta during December 1-18, 2007 .................................................................... 74
Figure 3.13(d). Back Trajectory Analysis of High PM2.5 in Quetta during December
1-18, 2007 ............................................................................................................... 75
Figure 3.14(a). Time Series of PM2.5 and Temperature in Lahore during June 1-12,
2007 ........................................................................................................................ 76
Figure 3.14(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore
during June 1-12, 2007 ........................................................................................... 77
Figure 3.14(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)
in Lahore during June 1-12, 2007 ............................................................................ 78
Figure 3.14(d). Back Trajectory Analysis of High PM2.5 in Lahore during June 1-12,
2007 ........................................................................................................................ 79
Figure 3.15(a). Time Series of PM2.5 and Temperature in Quetta during August 13-
19, 2007 .................................................................................................................. 80
xi
Figure 3.15(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta
during August 13-19, 2007 ...................................................................................... 81
Figure 3.15(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)
in Quetta during August 13-19, 2007 ....................................................................... 81
Figure 3.15(d). Back Trajectory Analysis of High PM2.5 in Quetta during August 13-
19, 2007…………………………………………………………………………...….82
Figure 4.1. Annual Averaged PM2.5 Mass Concentration in Islamabad during
2007… .................................................................................................................... 86
Figure 4.2. Annual Averaged Concentration of NO (µg m-3) in Islamabad during
2007-2011 ............................................................................................................... 86
Figure 4.3. Annual Averaged Concentration of CO (mg m-3) in Islamabad during
2007-2011 ............................................................................................................... 87
Figure 4.4. Annual Averaged Concentration of O3 (µg m-3) in Islamabad during
2007-2011 …..………………………………………………………………………..87
Figure 4.5. Number of Exceedances of Annual Average Concentration of CO (mg
m-3) in Islamabad during 2007-2011……………………………………………..…..88
Figure 4.6. Number of Exceedances of Annual Average Concentration of O3 (µg m-3)
in Islamabad during 2007-2011 ............................................................................... 88
Figure 4.7. Time Series of Ambient Concentrations of O3, NO, SO2, PM2.5 and CO in
Islamabad during 2007-2011 ................................................................................... 89
Figure 4.8. Time Series of Monthly Averaged Concentrations of O3, NO, SO2, PM2.5
and CO in Islamabad during 2007-2011 .................................................................. 90
Figure 4.9. Correlation between CO and PM2.5 ambient concentration during 2007-
2011…..………………………………………………………….…………...............91
Figure 4.10. Correlation between CO and NO in Islamabad during 2007-2011 ....... 92
Figure 4.11. Correlation between CO and NOy′ in Islamabad during 2007-2011…..92
Figure 4.12. Monthly average of SO2 concentration for 2007, 2008, 2010, and 2011
(I denotes ±1SD) ..................................................................................................... 95
Figure 4.13. Correlation between SO2 and NO in Islamabad during 2007-2011 ...... 95
xii
Figure 4.14. Correlation of PM2.5 and NO in Islamabad for the Period 2007-2011….
................................................................................................................................ 96
Figure 4.15(a). Correlations between Measured Daily Averages of CO and PM2.5 in
Islamabad during 2007-2011…………………………………………………………97
Figure 4.15(b). Correlations between Measured Daily Averages of NO and SO2 in
Islamabad during 2007-2011…………………………………………………………98
Figure 4.15(c). Correlations between Measured Daily Averages of CO and O3 in
Islamabad during 2007-2011…………………………………………………………98
Figure 4.15(d). Correlations between Measured Daily Averages of CO and NOy′ in
Islamabad during 2007-2011……………………………………………………...….99
Figure 4.15(e). Correlations between Measured Daily Averages of NMHCs and O3 in
Islamabad during 2007-2011…………………………………………………………99
Figure 4.15(f). Correlations between Measured Daily Averages of CH4 and O3 in
Islamabad during 2007-2011…………………………….………………………….100
Figure 4.16. Variation of concentration of Ozone vs (NOy’-NO)/NOy’ in the summer
months for 2007-2011 during maximum photochemical activity of the day i.e., 9:00
a.m. to 3:00 p.m. ………………………………...………………………………….101
Figure 4.17(a). Diurnal profiles of ozone, nitric oxide, CO and non-methane
hydrocarbons (NMHCs) ………………………...………………………………….102
Figure 4.17(b). Seasonal and diurnal variation of averaged ozone concentration
during 2007-2011 (±1 standard deviation is also shown in the figure)…………..…103
Figure 4.18(a). Correlation of Ozone with Temperature during 2007-2011 at 9:00
a.m. – 3:00 p.m. ……………...……………………………………………………..104
Figure 4.18(b). Correlation of Ozone with Solar Radiation during 2007-2011 at 9:00
a.m. – 3:00 p.m. …...………………………………………………………………..105
Figure 4.18(c). Correlation of PM2.5 with Temperature during 2007-2011 at 9:00 a.m.
– 3:00 p.m. ………………………………………………………...………………..105
Figure 4.18(d). Correlation of PM2.5 with Solar Radiation during 2007-2011 at 9:00
a.m. – 3:00 p.m. ……...……………………………………………………………..106
xiii
Figure 4.19. Air Parcel Back Trajectories for PM2.5 and Ozone Episodes during 2007-
2011 ...................................................................................................................... 109
Figure 4.20. Air parcel 48-hour back trajectories analysis for some selected
PM2.5 and Ozone high pollution episodes during 2007-2011 .................................. 110
Figure 5.1. Back Trajectory Analysis of High Ozone Episode in Islamabad during
27th August – 2nd September, 2007………………………………………..………..114
Figure 5.2. Back Trajectory Analysis of High Ozone Episode in Islamabad during
September 7-19, 2007…………………………………...………………………….115
Figure 5.3. Back Trajectory Analysis of High Ozone Episode in Islamabad during
September 25-27, 2007………………..……………………………………………116
Figure 5.4. Back Trajectory Analysis of High Ozone Episode in Islamabad during
October 12-21, 2007…………….…………………………………………………..117
Figure 5.5. Back Trajectory Analysis of High Ozone Episode in Islamabad during
28th April – 1st May, 2008………..…………………………………………………118
Figure 5.6. Back Trajectory Analysis of High Ozone Episode in Islamabad during
10th May – 1st June, 2008………………………..………………………………….119
Figure 5.7. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 4-13, 2008.................………………………………………………………….120
Figure 5.8. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 20-24, 2008………...………………………………………………………….121
Figure 5.9. Back Trajectory Analysis of High Ozone Episode in Islamabad during
August 25-27, 2008…………………………………………………………………122
Figure 5.10. Back Trajectory Analysis of High Ozone Episode in Islamabad during
May 13-21, 2009……..……………………………………………………………..123
Figure 5.11. Back Trajectory Analysis of High Ozone Episode in Islamabad during
May 6-31, 2009……………………………...……………………………..……….124
Figure 5.12. Back Trajectory Analysis of High Ozone Episode in Islamabad during
August 7-9, 2009……………………………………………………………………125
xiv
Figure 5.13. Back Trajectory Analysis of High Ozone Episode in Islamabad during
August 22-25, 2009…………………………………………………………………126
Figure 5.14. Back Trajectory Analysis of High Ozone Episode in Islamabad during
August 27-30, 2009 ............................................................................................... 127
Figure 5.15. Back Trajectory Analysis of High Ozone Episode in Islamabad during
September 19-22, 2009 .......................................................................................... 128
Figure 5.16. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 11-13, 2010 ................................................................................................... 129
Figure 5.17. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 19-23, 2010………………………………………………………………..…..130
Figure 5.18. Back Trajectory Analysis of High Ozone Episode in Islamabad during
April 22-24, 2011…………………….……………………………………………..131
Figure 5.19. Back Trajectory Analysis of High Ozone Episode in Islamabad during
May 16-20, 2011 ................................................................................................... 132
Figure 5.20. Back Trajectory Analysis of High Ozone Episode in Islamabad during
May 22-25, 2011……………………………………………………………...….…133
Figure 5.21. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 2-25, 2011 ..................................................................................................... 134
Figure 5.22. Back Trajectory Analysis of High Ozone Episode in Islamabad during
July 4-6, 2011 ........................................................................................................ 135
Figure 5.23. Back Trajectory Analysis of High Ozone Episode in Islamabad during
July 10-13, 2011…………………………………………………………………….136
Figure 5.24(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009........ 138
Figure 5.24(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 09, 2009........ 138
Figure 5.24(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009........ 139
xv
Figure 5.24(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 09, 2009........ 139
Figure 5.25(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 10, 2009........ 141
Figure 5.25(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 10, 2009........ 141
Figure 5.25(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 10, 2009........ 142
Figure 5.25(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 10, 2009........ 142
Figure 5.26(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 11, 2009........ 143
Figure 5.26(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 11, 2009........ 143
Figure 5.26(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 11, 2009........ 144
Figure 5.26(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 11, 2009........ 144
Figure 5.27(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 12, 2009........ 146
Figure 5.27(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 12, 2009........ 146
Figure 5.27(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 12, 2009........ 147
Figure 5.27(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 12, 2009........ 147
Figure 5.28(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 13, 2009........ 148
xvi
Figure 5.28(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 13, 2009........ 148
Figure 5.28(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 13, 2009........ 149
Figure 5.28(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 13, 2009........ 149
Figure 5.29(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 14, 2009........ 151
Figure 5.29(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 14, 2009........ 151
Figure 5.29(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 14, 2009........ 152
Figure 5.29(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 14, 2009........ 152
Figure 5.30(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 15, 2009........ 153
Figure 5.30(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 15, 2009. ....... 153
Figure 5.30(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 15, 2009. ....... 154
Figure 5.30(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 15, 2009. ....... 154
Figure 5.31(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011.... 156
Figure 5.31(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 15, 2011.... 156
Figure 5.31(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011.... 157
xvii
Figure 5.31(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 15, 2011.... 157
Figure 5.32(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 16, 2011.... 158
Figure 5.32(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 16, 2011.... 158
Figure 5.32(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 16, 2011.... 159
Figure 5.32(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 16, 2011.... 159
Figure 5.33(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 17, 2011.... 161
Figure 5.33(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 17, 2011.... 161
Figure 5.33(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 17, 2011.... 162
Figure 5.33(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 17, 2011.... 162
Figure 5.34(a). Daytime Averaged Air Temperature (oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 18, 2011.... 163
Figure 5.34(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 18, 2011.... 163
Figure 5.34(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 18, 2011.... 164
Figure 5.34(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 18, 2011.... 164
Figure 5.35(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 19, 2011.... 165
xviii
Figure 5.35(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 19, 2011.... 165
Figure 5.35(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 19, 2011.... 166
Figure 5.35(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on August 19, 2011.... 166
xix
LIST OF TABLES
Table 3.1 Linear Regression Analysis of PM2.5 and Meteorological Variables for Islamabad, Lahore, Peshawar, and Quetta………………………………...61
Table 4.1 Ratio Analysis based on average emissions and/or ambient data……….94
Table 4.2 Linear Regression Analysis of Ozone and PM2.5 with Meteorological Variables ………………………………………………………………..107
xx
1
CHAPTER 1: INTRODUCTION
1.1. Urban Air Pollution
Air pollution is defined as “the presence of substances in the ambient
atmosphere resulting from the activity of man or from natural processes causing
adverse effects to man and the environment” (Weber, 1982). Presently, the urban air
quality has been degraded to a great extent due to indiscriminate industrialization,
vehicular emissions and biomass burning (Jenkin, 2004; Real and Sartelet, 2011). It is
important to understand the complexities of air quality in order to deal with the
climate change. Air pollution is caused by emissions from various natural and
anthropogenic sources and it depends on meteorological conditions and topography.
Air pollution is controlled mainly through controlling various emission sources. Rapid
urbanization and economic growth has led to air pollution with an increase in
pollutants like particulate matter, NOx, SO2, CO, NMHCs etc. These pollutants are
either emitted directly into the atmosphere or formed in the troposphere by
photochemical reactions. Some of these pollutants like CO, NOx and NMHCs are
precursors for photochemical production of ozone in the troposphere. It has been
observed that the atmospheric concentrations of various pollutants are increasing
continuously. For instance, the concentration of tropospheric ozone has increased by a
factor of 3-4 during last century. Similar changes have been observed in the
concentration of N2O with an increase of 15% during last 150 years. The
concentration of methane has been doubled during last 150 years due to increase in
anthropogenic sources. Hydrocarbons are also showing the similar pattern of growth
(IPCC, 2001). It has been estimated that approximately 3333 tons of organic
compounds and 890 tons of NOx are released into the basin of Los Angeles on daily
basis (Monks and Leigh, 2009).
A severe form of air pollution is known as smog which comes from
combination of smoke and fog. The photochemical smog such as experienced in Los
Angeles is caused by high concentration of oxidants such as ozone and peroxidic
compounds which are formed by photochemical reactions (Monks and Leigh, 2009).
2
Air pollutants have a long-range transport mechanism which leads to regional and
global impacts of air pollution (Zhao et al., 2007).
1.2. Major Air Pollutants and Tropospheric Chemistry
1.2.1 Sulfur Dioxide
Sulfur compounds in the atmosphere originate from natural as well as
anthropogenic sources. However, anthropogenic emission accounts much more than
the sulfur concentration released from natural sources. It has been estimated that
about 75% of the average global levels of sulfur and about 90% of sulfur in the
Northern Hemisphere are coming from anthropogenic activities. Natural sources of
sulfur are soil, volcanic eruption and biogenic activities in the ocean (Jackson, 2003).
Major anthropogenic sources of SO2 are industrial and transport sector (Harrison et
al., 1997). Brick kilns are also considered to be a major source of SO2 in Pakistan as
coal with high sulfur content is used as fuel (Biswas et al., 2008). Sulfur dioxide in
the atmosphere leads to formation of PM2.5 and acid deposition (Wang and Wang,
1995).
Sulfur dioxide does not dissociate photochemically in the atmosphere. The
oxidation of sulfur compounds causes major concerns like climate balance and
acidification. Furthermore, high sulfur concentrations lead to exchange of sulfates
from troposphere to stratosphere forming a layer in the stratosphere (Monks and
Leigh, 2009). The oxidation of SO2 is carried by various mechanisms in liquid- and
gas-phase. The gas-phase oxidation reactions of sulfur dioxide are mentioned as
follows (Monks and Leigh, 2009):
The gas-phase oxidation of SO2 leads to formation of sulfuric acid which gets
attached to the particulate matter due to its relatively low vapour pressure. A major
fraction of sulfuric acid is removed by wet deposition process through precipitation
3
and cloud droplets (Monks and Leigh, 2009). The aqueous phase oxidation of SO2
depends on the nature of phase such as clouds and fog and availability of light and
oxidants like ozone and H2O (Monks and Leigh, 2009). The wet and dry depositions
cause corrosion and damage to crops, forests and aquatic ecosystems (Monks and
Leigh, 2009). Sulfur dioxide and nitrogen dioxide contribute to acid rain by adding
sulfuric acid and nitric acid to the atmosphere (Monks and Leigh, 2009).
1.2.2 Carbon Monoxide
Carbon monoxide is produced through inefficient burning of fossil fuel. Major
sources of CO are combustion from internal engines, indoor cooking and heating and
iron smelting, burning of biomass and oxidation of non-methane hydrocarbons
(NMHCs) and methane (Ayres and Ayres, 2009; Wotawa et. al., 2001) with the
largest source coming from tropospheric photochemical reactions i.e., 25 times higher
than combustion (Weinstock and Niki, 1972). Burning of agricultural residues and
forest fires also contribute to the emission of carbon monoxide in the troposphere
(Kasischke and Bruhwiler, 2003; Crutzen and Andreae, 1990). Carbon monoxide is
used as an indicator of presence of other pollutants such as ozone in the troposphere
(Zhao et al., 2007). It is also a good tracer of pollutants coming out of sources like
incomplete combustion of fossil fuel particularly motor vehicles (Zhao et al., 2007).
Lifetime of carbon monoxide in the troposphere is from a few weeks to several
months due to which it is transported over long distances causing pollution to the
regions far away from the emission sources (Zhao et al., 2007; Liu et al., 2002). It has
been reported by Poisson et al. (2000) that the oxidation of NMHCs is responsible for
approximately 40 - 60% of the concentration of carbon monoxide over oceans and
about 30 - 60% of carbon monoxide in the atmosphere. CO is an important variable as
it can be used as an indicator of mobile source emissions (Warneck, 1988).
Furthermore, it is the precursor gas for ozone formation in the troposphere (Warneck,
1988). In relatively less polluted areas, i.e., low NOx regimes, hydroxyl radical reacts
with carbon monoxide to form peroxy radical:
4
In low-NOx conditions, HO2 reacts with ozone causing its destruction and also
leads to formation of OH radical:
In polluted areas, HO2 reacts with another peroxy radical leading to the formation of hydrogen peroxide (H2O2):
It may also react with organic peroxy radical (CH3O2) to form organic peroxides:
In high NOx-areas, peroxy radical catalyses the oxidation of NO to NO2 with subsequent formation of ozone:
1.2.3 Methane
Wetlands, oceans, geology and termites are the natural sources of methane in
the atmosphere (IPCC, 2001). Other than the natural sources, methane is emitted from
landfills, natural gas, coal combustion, petroleum industries, rice paddies, biomass
burning and enteric fermentation (Forster et al., 2007; Mathez, 2009; IPCC, 2001).
Methane is present in the atmosphere much more than any other organic specie
(Shallcross, 2009). The global average concentration of methane was 750 ppbv in the
pre-industrial era which has now increased to 1780 ppbv in the Northern Hemisphere
and 1650 ppbv in the Southern Hemisphere (Shallcross, 2009) with the global average
5
concentration as 1775 ppbv (Mathez, 2009). During 20th century, methane had been
increasing about 1.3% annually till early 1990s (Blake and Rowland, 1988), however,
the growth rate of global methane concentration slowed down to about 0.6% on
annual basis (Steele et al., 1992). The actual reason of slowing down of methane
growth rate in the atmosphere is not confirmed as it may be either because of
reduction in emission sources of methane or its removal by radical chemistry which
involves reaction with hydroxyl radical (Shallcross, 2009).
In low-NOx areas, methane reacts with hydroxyl radical to form peroxy radical CH3O2
which facilitates the oxidation of NO to NO2 which then leads to photochemical
production of ozone as given in reactions (1.11) and (1.12):
1.2.4 Non-Methane Hydrocarbons
The ambient concentrations of non-methane hydrocarbons (NMHC) and NOx
along with some catalysts contribute to the difference between urban and rural
photochemistry (Shallcross, 2009). Hydrocarbons are very important in the
tropospheric ozone formation contributing to about 40% of ozone in the troposphere
(Houweling et al., 1998). The NMHCs in the troposphere come from various natural
and anthropogenic sources mainly coming from automobile exhaust, oil-fired power
plants, biomass burning, combustion processes, oil-based paints, petrochemical
industries, solvent makers, evaporation of gasoline and leakages from natural gas and
liquified petroleum gas (An et al., 2008; Arsene et al., 2009; Harrison, 1999; Jackson,
2003; Watson et al., 2001). The natural sources of NMHCs include biogenic and
oceanic emissions (Arsene et al., 2009; Harrison, 1999). Reactive hydrocarbons
mainly come from biogenic sources (Hewitt, 1999).
It has been observed that aldehydes increase the rate of conversion of NO to
NO2 and consequently the formation of ozone (Wayne, 2000). Hydroxyl radical also
comes from the photolysis of aldehydes and ketones formed by the oxidation of
6
NMHCs (Monks and Leigh, 2009). Reaction of NMHCs with OH radical is very
important due to its role in photochemistry of the troposphere (Derwent, 1995;
Finlayson-Pitts and Pitts, 1999). The oxidation of NMHCs is initiated by hydroxyl
radicals which lead to oxidation of NO to NO2. NO2 is then photolyzed to form
tropospheric ozone. In these reactions, hydroxyl radical is catalytic and peroxy
radicals are the chain propagators. The final oxidized products of hydrocarbons are
water vapours, whereas, some other partially oxidized species like aldehydes, ketones
and carbon monoxide are also formed with ozone as a byproduct (Monks and Leigh,
2009). The schematic representation of these reactions is given below:
Reaction (1.20) then leads to photochemical formation of ozone through
reactions (1.11) and (1.12).
Major issue of concern is the health effects of NMHCs as some of these
compounds are carcinogenic (Jackson, 2003). There have been increased incidences
of lungs cancer, tumors of skin and bladder due to increased exposure to PAHs
through inhaled air (Monks and Leigh, 2009). Atmospheric hydrocarbons lead to
ozone formation by reacting with OH radicals in the presence of NOx and also
contribute to the formation of particulate matter through gas to particle conversion
(Arsene et al., 2009; Claeys et al., 2004; Atkinson and Arey, 2003).
1.2.5 Hydroxyl Radical
Hydroxyl radical is the most reactive specie in the troposphere and is an
indicator of radical-chain oxidation reactions in the atmosphere. A major source of
hydroxyl radical is photolysis of ozone involving water vapours in the atmosphere
(Monks and Leigh, 2009). The most critical aspect of hydroxyl is its abundant
concentration in the atmosphere and its high reactivity. In the areas with no pollution
7
i.e., low NOx, OH radical reacts with carbon monoxide or methane to form carbon
dioxide and peroxy radicals like CH3O2 and HO2. HO2 further reacts with ozone to
form OH radical. In this way, OH radical leads to reactions which contribute to
destruction of tropospheric ozone (Monks and Leigh, 2009).
1.2.6 Nitrogen Oxides
There are a number of anthropogenic and biogenic sources for nitrogen oxides
in the atmosphere which include biomass burning, vehicular and industrial emissions,
fossil fuel combustion, emissions from the power plants, lightening discharges,
agricultural activities and microbial activity in soil (Shallcross, 2009; Mathez, 2009;
Finlayson-Pitts and Pitts, 2000). The fate and influence of NOx depend on its sources
and sinks in the atmosphere. The lifetime of NOx is quite significant and depends on
[NO/NO2] ratio and concentration of hydroxyl radical (Shallcross, 2009). The sum of
total reactive nitrogen is termed as NOy which is defined as NOy = NOx + NO3 +
2N2O5 + HNO3 + HNO4 + HONO + PAN + nitrate aerosol + alkyl nitrate. Nitrogen
oxides are also converted into nitric acid and nitrate particulates which may be
removed from the atmosphere by wet and dry deposition processes (Monks and
Leigh, 2009). Peroxy Acetyl Nitrate (PAN) is a byproduct of oxidation linked to
urban air pollution. It is an important component of NOy as it transports nitrogen
oxides from urban polluted areas to the remote areas (Singh et al., 1992).
NO, NO2 and ozone are considered to be in a photostationary state under
suitable concentrations (Leighton, 1961) with the condition that these are not affected
by the local emission sources of NOx and that the constant solar radiation is available
(Shallcross, 2009). Night-time chemistry is significant due to its role in formation of
secondary pollutants. During night-time, gradual oxidation of NO2 by ozone leads to
formation of secondary pollutants (Shallcross, 2009). The following reactions show
the photostationary state of NOx and ozone:
8
1.2.7 Particulate Matter in the Atmosphere
Particulate matter with aerodynamic diameter less than 10µm are known as
coarse particulates (PM10), whereas, particulates with aerodynamic diameter less than
2.5 µm are called fine particulate matter (PM2.5). Composition of particulate matter
depends on the source characteristics (McMurry et al., 2004). Particulate matter (PM)
include nitrates, sulfates, elemental carbon, organic carbon, sea salt and soil dust.
Nitrates, sulfates and carbon particulates are present as PM2.5, whereas, the others are
coarse particles. Black carbon is emitted directly into the atmosphere as primary
pollutant, whereas, nitrates, sulfates and organic carbon are formed in troposphere by
oxidation of nitrogen oxides, sulfur dioxide and non-methane hydrocarbons
respectively (Jacob and Winner, 2009). Organic carbon and nitrates keep on changing
their phase between gas and particle form depending upon temperature variation
(Jacob and Winner, 2009). Significant anthropogenic sources of particulate matter
include coal combustion, motor vehicles, industrial activities, cement production,
incineration metallurgy, biomass burning and agricultural activities (Jackson, 2003;
Biswas et al., 2008). Particulate matter also include some of toxic compounds like
polycyclic aromatic hydrocarbons (Smith et al., 1996).
The adverse health effects of particulate matter include pre-mature mortality,
lungs and cardiovascular diseases (McMurry et. al., 2004; Pope et al., 2002). Fine
particulate matter is very significant in tropospheric chemistry, whereas, larger
particles provide surface for heterogeneous reactions and also perform the role of
cloud condensation nuclei (Monks and Leigh, 2009).
Particulate Matter also have impact the formation of clouds and weather
pattern. It has been observed that the areas with more haze have less rainfall
(Rosenfeld et al., 2007). Particulate Matter is important factor for climate change due
to its contribution to radiation transfer in the atmosphere (IPCC, 2001). Particulate
Matter also affect the hydrological cycle and rate of precipitation (Ramanathan et al.,
2001; Menon et al., 2002; Sarkar et al, 2006).
9
Black carbon is emitted into the atmosphere from combustion of fossil fuels
particularly diesel, forest fires and agricultural residue burning (Husain et al., 2007).
Black carbon consist of both elemental and organic carbon species (Andreae and
Gelencser, 2006). About 5-15% of PM2.5 in the atmosphere is the black carbon
(Husain et al., 2007). Black carbon aerosols contribute to the global warming to a
great extent due to their capacity to absorb the solar radiation (Jacobson, 2002). It has
been reported that the high black carbon concentrations have led to increased flooding
and droughts in South Asian region (Menon et al., 2002). Furthermore, such high
black carbon concentrations have also caused the loss of agricultural productivity by
10-20% in some countries of South and Southeast Asia (Chameides et al., 1999).
Black carbon particles can travel upto thousands of kilometers due to their residence
time of six days (Husain et al., 2007). Black carbon particulates contribute to global
warming through radiative forcing (Penner et al., 2003). Dust particles add heat to the
atmosphere through absorption of solar radiation (Haywood et al., 2001). Brick kilns
are another significant source of black carbon along with polycyclic aromatic
hydrocarbons (PAHs) and sulfur (Biswas et al., 2008; Smith et al., 1996) into the
atmosphere. Inefficient fuel usage in brick kilns like low quality coal, old tyres and
biomass leads to toxic emissions (Stone et al., 2010).
During past two decades, some areas of Northeastern Pakistan and Eastern
India have been experiencing severe fog in winter during which high concentration of
sulfur dioxide has been observed (Hameed et al., 2000). The average concentration of
PM2.5 in Lahore has been observed to be manifold higher than the average PM2.5
concentrations in New York, Seoul and Hong Kong (Biswas et al., 2008). Husain et al
(2007) has reported the average PM2.5 mass concentration as 190 µg m-3 with
variation of 89 - 476 µg m-3 in Lahore, 67% of which is carbonaceous in nature.
Increased emissions of its precursor gases is of great concern (Ohara et al., 2007)
particularly in South Asian region where high altitude areas of Himalayas are
characterized by the severe pollution phenomenon of Atmospheric Brown Clouds
(UNEP, 2008). The high concentration of aerosols has huge impact on the air quality
as well as to the climate change (UNEP, 2008).
Adverse health effects of particulate matter include asthma, pneumonia,
exacerbation of chronic obstructive pulmonary disease, increased mortality rate and
10
decreased lungs function (USEPA, 1995; Saldiva et al., 1995). Approximately, annual
3, 60,000 premature deaths in Asia are caused by exposure to PM2.5 (WHO, 2008).
Fine particulate matter increases the risk of respiratory and cardiovascular diseases by
penetrating into lungs (Schwartz et al., 2002; Dockery et al., 1993).
1.2.8 Tropospheric Ozone Formation
Tropospheric ozone adversely affects the life on earth due to its reactive nature
(Mathez, 2009). Ozone has been designated as criteria pollutant by USEPA due to its
health effects (USEPA, 1993). It is very reactive and, therefore, does not stay in the
troposphere for long time (Mathez, 2009). Ozone is formed photochemically in the
atmosphere by carbon monoxide, nitrous oxide and hydrocarbons (Mathez, 2009) in
presence of HOx (Jacobson, 2002; Chan et al 1998; Crutzen, 1973; Chameides and
Walker, 1973). Previously, it was considered that the stratospheric ozone is the major
source of tropospheric, however, later it was found that the photochemical oxidation
of various gases in the atmosphere is a major source of ozone (Shallcross, 2009;
Fabian and Pruchniewz, 1977). Ozone titration by nitrogen oxides brings balance to
its atmospheric concentration (Shallcross, 2009). Tropospheric ozone, hydroxyl
radical and H2O2 are the indicators of oxidizing capacity of the troposphere
(Shallcross, 2009). Ozone formation may be either NOx- or VOC- sensitive and it
basically depends on VOC/NOx ratio (Sillman, 1999).
Ozone is a very reactive gas and, therefore, it poses high risk to human health
(Conti et al., 2005) and ecosystem (Paoletti et al., 2006). Tropospheric ozone
contributes to the global radiative forcing which makes it a significant greenhouse gas
(Forster et al., 2007). Stratosphere-Troposphere Exchange also contributes to the
tropospheric ozone concentration. (Davis et al., 2010; Trickl et al., 2010). Long range
transport and variation in lifetime of the ozone lead to its spatial and temporal
variation in the troposphere (Mickley et al., 2004).
Atmospheric dynamics is quite significant in variation of ozone concentration
in the troposphere (Delcloo and Backer, 2008). Ozone exchange in stratosphere-
troposphere and troposphere-PBL also contributes partially to the natural variation of
ozone in the troposphere (Stohl et al., 1995). Reduction in emission of precursor gases
11
has led to low tropospheric ozone concentrations in Europe (Delcloo and Backer,
2008; Bronniman et al., 2003). It may be explained by the fact that 30% and 15-20%
of the anthropogenic emissions of NOx and NMHCs respectively was reduced in
Europe and North America during 1990-2002 (Delcloo and Backer, 2008). About
45% of the emissions of carbon monoxide have been reduced in Europe during this
period, whereas, emissions are increasing in Asia with no check (Streets et al., 2003).
Being a precursor gas for formation of highly reactive hydroxyl (OH) radical,
ozone has a significant role in global climate change (Kumar et al., 2010). High ozone
concentrations have been observed in Asian region where it poses great risk to human
health and plants (Mauzerall and Wang, 2001; Desqueyroux et al., 2002; Tanimoto,
2009]. It has been reported (Ding et al., 2008) that there has been an approximately
2% increase in ozone concentration in Beijing during 1995-2000. In Southern China,
an annual increase of 0.5-0.9 ppbv (Wang et al., 2009) and a monthly increase of 5%
have been reported for Eastern China (Xu et al., 2008). High ozone concentration at
low altitude areas is linked to the in situ photochemical formation involving
precursors and radiation (Solberg et al., 2008).
Ozone has very low solubility in water and, therefore, ozone is not removed
by wet deposition process (Jacob and Winner, 2009). Ozone, along with its precursor
gases, is transported from polluted areas to far-off regions due to its lifetime of days
to weeks affecting the regional background ozone concentration which is of great
concern (Jacob and Winner, 2009).
1.3 Air Pollution and Meteorology
Meteorology affects the quality of air by interrupting the rate of dispersion
with change in wind speed, convection and mixing depth, precipitation scavenging,
dry deposition and rate of photochemical reactions. Meteorological conditions like
temperature, relative humidity, stagnation, wind speed, wind direction, precipitation,
convection, mixing depth and boundary layer mixing also affect the air quality (Lin et
al., 2001; Jacob and Winner, 2009). Interannual variability of ambient concentration
of pollutants in relation to the meteorological conditions has been reported. Climate
change has the tendency to increase the frequency of tropospheric ozone pollution
12
episodes. Weather conditions like high temperature and stagnation have been held
responsible for the highest ozone episode in northeastern USA (Lin et al., 2001).
Northeast US experienced the highest temperature during summer 1988 and very high
number of ozone exceedances during this period. Such exceptionally ozone episodes
due to an increase of global temperature would cause implications for air quality. It
has been observed that the stagnation causes less dispersal of air pollutants causing
higher concentration of pollutants (Holzer and Boer, 2001). Eastern US experienced
severe and persistent pollution episodes due to decreased ventilation by cyclones
tracking across Canada (Leibensperger et al, 2008; Mickley et al., 2004). Europe has
also experienced very high ozone episodes during heat wave of summer 2003 which
implies the role of temperature in ozone formation (Solberg et al., 2008; Vautard et
al., 2007).
Low wind speed restricts dispersion of air pollutants leading to their
accumulation in a particular area (Holzer and Boer, 2001). High temperature gives
rise to the formation of ozone due to increased rate of photochemical reactions and
increased biogenic emissions consequently affecting the formation of ozone (Kleeman
et al., 2010; An et al., 2008; Lin et al., 2001). Ozone has a strong correlation with
ambient temperature (Cox and Chu, I995) and a negative correlation with relative
humidity (Camalier et al., 2007). However, ozone concentration of less than 60 ppbv
has not shown correlation with temperature (Sillman and Samson, 1995). It has been
estimated that there will be a 10% rise in tropospheric ozone concentration with a rise
of 4oC in average annual temperature (US-EPA, 1989). Various models simulations
have confirmed that temperature is affecting the ozone formation more significantly
(Steiner et al., 2006; Dawson et al., 2007). It has been projected for four cities in
Canada with different weather conditions that high ozone episodes would increase by
50% in 2050s and 80% in 2080s (Cheng et al., 2007).
Major factors contributing to ozone formation in the troposphere are
photochemical processes, horizontal and vertical transportation and physical removal
mechanisms (Altshuller, 1989). Ambient meteorological conditions i.e., radiation
levels, temperature, and humidity influence the level of tropospheric ozone (Seinfeld
and Pandis, 1998; Chan et al., 1998). High ozone episodes are associated with
stagnation, high temperature, low relative humidity (Ellis et al., 2000) and anti-
13
cyclones (Cheng et al., 2007). Topography also plays a major role in high ozone
concentrations as subsidence inversion is formed in specific areas (Cheng et al., 2001;
Tanner and Law, 2002). The influence of wind direction varies due to the advection of
air masses, topography and nearby high ozone areas (Rohli et al., 2004). Tropospheric
ozone may travel to hundreds of kilometers downwind depending on the wind speed
and direction and topography (An et al., 2008).
Precipitation is a major sink for particulate matter which reduces its lifetime in
the troposphere (Jacob and Winner, 2009). High wind speed is associated with less
pollutants due to dispersal and transportation to other regions (Husain et al., 2007),
however, it may also bring pollutants from other areas. Low mixing height during
nighttime leads to buildup of pollutants (Husain et al., 2007). During winter season,
lower ambient temperature, low wind speed and the height of boundary layer i.e.,
~500-800 meters lead to accumulation of air pollutants (Ram et al., 2010; Nair et al.,
2007). Particulate matter is negatively correlated with relative humidity (Wise and
Comrie, 2005). Nitrate particles decrease with increase in temperature as they
exchange between gas and particle phase (Tsigaridis and Kanakidou, 2007). Sulfate
particles increase with increase in temperature (Dawson et al., 2007; Kleeman, 2007)
as a result of increased rate of sulfur oxidation. PM mass concentration decreases with
increase in precipitation due to removal by wet deposition process (Balkanski et al.,
1993).
Particulate matter and ozone are known as significant climate forcing variables
due to their interaction with the solar radiation (Forster et al., 2007). It has been
observed that about 80% of the variation in average ozone concentration is positively
correlated with temperature and negatively correlated with the relative humidity
(Camalier et al., 2007). Solar radiation and temperature have been found to be the
dominant meteorological variables affecting ozone concentrations during summer
season in Switzerland (Ordonez et al., 2005).
Particulate matter is less correlated with meteorological conditions than ozone
due to variation in its composition (Wise and Comrie, 2005). Particulate matter has a
strong negative correlation with wind speed (Cheng et al., 2007). Some of the areas
14
experience slowly moving anticyclones which lead to accumulation of air pollutants
(Hulme and Jenkins, 1998).
1.4 Air Pollution and Climate Change
According to IPCC (2007), “Climate Change is referred to a change in the
state of climate that can be identified (e.g., using statistical tests) by changes in the
mean and/or the variability of its properties, and that persists for an extended period,
typically decades or longer. It refers to any change in climate over time, whether due
to natural variability or as a result of human activity”. This definition differs from that
in the United Nations Framework Convention on Climate Change (UNFCCC), where
climate change refers to a change of climate that is attributed directly or indirectly to
human activity that alters the composition of the global atmosphere and that is in
addition to natural climate variability observed over comparable time periods”.
Air Quality has influenced the global climate to a great extent. Some of the
significant factors which cause climate change are greenhouse gas concentrations,
palaeogeography, variation in ocean-heat transport and changing orbital parameters.
Climate system consists of atmosphere, cryosphere, hydrosphere, biosphere and
lithosphere. Almost all the energy required by the climate system is provided by the
solar radiation. The spherical shape of Earth is responsible for seasonal cycle of
weather (Lockwood, 2009). Indiscriminate energy usage causes rapid increase in air
pollution and subsequently leads to climate change. Global warming started in 1910
proceeding in two phases i.e., during 1910-1940 and then starting again in 1970 till
today (Mathez, 2009). Global average temperature increase during last century is
about 1oC, whereas, the rate of increase during last three decades has been 0.27oC per
decade (Brohan et al., 2006). This global warming is causing sea level rise at the rate
of 2.6±0.04 mm per year which leads to sea level rise of 26 cm (10 inches) per
century (Alley et al., 2005). It has been estimated that the sea level may increase upto
20-60 centimeter (8-24 inches) by 2100 (Meehl et al., 2007).
Global average mixing ratio of CO2 has been increased by about 36% after
industrial revolution with an increase from 285 ppm in pre-industrial era to 379 ppm
in 2005 (Forster et al., 2007). Major sources of CO2 are combustion of fossil fuels,
15
biomass burning and deforestation (Forster et al., 2007; Houghton, 2003; Andreae and
Merlet, 2001). Chlorofluorocarbons are about ten times stronger than the CO2
greenhouse effect due to its role in stratospheric ozone depletion (Ramanathan and
Feng, 2009). Man-made fluorine gases have increased efficiency to absorb infrared
radiation. Perfluorocarbons (PFCs) are highly radiative efficient with a lifetime of
1,000 to 50,000 years (Forster et al., 2007). Nitrogen oxides are also important factors
for depletion of stratospheric ozone which consequently leads to an imbalance of
absorbed and emitted solar radiation (Crutzen, 1972). Nitrous oxide (N2O), another
greenhouse gas, increased from 270 ± 7 ppbv in pre-industrial era to 314 ppb in the
year 1998 (Forster et al., 2007).
It has been observed that the annual average minimum and maximum
temperature have increased and that the average minimum temperature has increased
more than the average maximum temperature (Vose et al., 2004). It has been
estimated that the global average temperature increase during 1910-1945 was 0.11oC
per decade, 0.01oC per decade from 1945 to 1975 and 0.22oC per decade during 1976-
2000 (Jones and Moberg, 2003). Global average temperature has been increased by
0.5oC during last 150 years (Houghton, 1994). Tropospheric ozone and particulate
matter are significant climate forcing agents due to their interaction with solar and
terrestrial radiation (Pulikesi et. al., 2006; Forster et al., 2007). The stratospheric
ozone depletion has led to an increase in the ground level radiation which
consequently leads to increase in the formation of tropospheric ozone due to increased
rate of photochemical reactions.
Global sea-surface temperature has also been increased about 0.7oC (1.3oF)
during past century, which is more than any other change in the climate system
(Trenberth et al., 2007; Barnett et al., 2005). It has been predicted that the average
temperature in Western United States will increase 0.8o-1.7oC by 2050 (Barnett et al.,
2008). It has been reported that the sixteen warmest years of the century have
occurred during last two decades with 2010 being the warmest year (WMO, 2011).
Ozone layer in the stratospheric protects the earth from harmful solar radiation
due to its capacity to absorb the radiation (Mathez, 2009). The atmospheric
compounds containing nitrogen, hydroxyl radicals and chlorine destroy the ‘good
16
ozone’ layer (Mathez, 2009). Volcanic eruption also emits chlorine and sulfur
compounds which ultimately reach stratosphere to destroy the ozone layer. Volcanic
eruption has been part of the natural processes balancing the concentration of ozone in
the stratosphere. However, after industrialization, anthropogenic activities are
contributing much more to the destruction of ozone layer (Bluth et al., 1993).
1.5 Air Pollution and Climate Change Scenario in Pakistan
Air pollution has become a major environmental concern for Pakistan. Major
causes for air pollution are rapid urbanization, economic growth and unplanned
industrialization. In urban areas of Pakistan, major sources of air pollutants are
automobiles exhaust, combustion of fossil fuels/biomass, coal-fired power plants,
industrial emissions; open burning of solid waste and aircrafts (IMF, 2010; Barber,
2008; World Bank, 2006). Industries leading to air pollution include power plants,
cement, steel, fertilizer, sugar mills along with a major contribution from the small-
scale industries like brick kilns and plastic moulding (IMF, 2010; ADB and CAI-Asia,
2006; Faruqee, 1997). Industrial emissions largely contain particulate matter, carbon
monoxide and carcinogens like soot and asbestos (IMF, 2010). The problem is
aggravated by old vehicles, diesel trucks and low level of fuel quality (World Bank,
2006). Vehicles with two-stroke engines contribute more to pollutants emissions due
to inefficiency of burning fuel. There has been a growth of 117% in production of
two-stroke vehicles in 2010-11 from year 2000-01 (Government of Pakistan, 2012).
Continued increase in the usage of diesel driven heavy duty vehicles and two-stroke
vehicles add up to emission of pollutants into the atmosphere (World Bank, 2006;
ADB and CAI-Asia, 2006). High sulfur content of 0.5-1% in diesel and 1-3.5% in
furnace oil leads to higher emissions of sulfur dioxide and particulates (ADB and
CAI-Asia, 2006). Particulate matter pollution is also aggravated by more reliance on
diesel fuel by the transport sector (Shyamsundar, et al., 2001). Compared to an
average vehicle of United States, an average Pakistani vehicle emits about 25 times
higher CO2 and CO, 20 times more non-methane hydrocarbons (NMHCs) and 3.5
times more sulfur dioxide (Barber, 2008). Usage of diesel-fueled electric generators at
a large scale due to extensive power outage in the country is a significant factor
adding up the urban air pollution (IMF, 2010).
17
The World Bank (2006) estimated an annual environmental degradation cost
of about Rs. 365 billion in Pakistan. Estimated environmental health cost due to urban
particulate pollution is about Rs. 65 billion causing about 22,000 premature annual
deaths among adults and 700 deaths among young children. Particulate matter is one
of the significant pollutants with adverse health impacts like pre-mature mortality,
lungs and cardiovascular diseases (NARSTO, 2004; Pope et al., 2002). An estimated
cost of about Rs. 45 billion is caused by lead exposure of which airborne lead
particulate is a major fraction. There is high incidence of IQ loss, mild mental
retardation, anemia and cardiovascular diseases caused by lead air pollution. Indoor
air pollution costs approximately Rs. 67 billion causing 28,000 deaths and 40 million
cases of respiratory diseases annually (World Bank, 2006).
Pakistan has become too vulnerable to climate change mainly due to its
geographical location despite the fact that it contributes least to the greenhouse gas
emissions (Ministry of Environment, 2011). Pakistan's ranking of global GHG
emitters is 135th with a contribution of 0.8% of total GHG emissions (Planning
Commission, 2010). In year 2008, Pakistan’s GHG emissions were 309 million
tonnes (mt) of CO2 equivalent, comprising of 54% CO2, 36% Methane (CH4), 9%
Nitrous Oxide (N2O) and 1% other gases. Energy is the largest sector contributing to
about 50% of the GHG emissions whereas, agriculture and industrial sectors have
share of 39% and 6% respectively. About 5% of the GHG emissions is being emitted
from other activities (Planning Commission, 2010). It has been projected that there is
an increased risk of flooding, glacier melting and landsliding in Pakistan due to
impacts of climate change (Planning Commission, 2010).
Average annual temperature increased by 0.6 °C over Pakistan during last
century with higher temperature increase over northern areas (0.8 °C) than that of
southern areas (0.5 °C). Another effect of climate change is an average increase in
precipitation by 25% in Pakistan. Future projections through Global Circulation
Models (GCMs) show that there will be an average increase in temperature over
Pakistan in the range of 1.3-1.5 °C by 2020s, 2.5-2.8 °C by 2050s, and 3.9-4.4 °C by
2080s compared to the average global temperature increase of 2.8-3.4 °C by the end
of the 21st century (Planning Commission, 2010). Due to increased trend of glacier
melting, fifty two lakes have been declared as potentially dangerous due to extreme
18
chances of outflow which may lead to flash floods (Ministry of Environment, 2011).
It is projected that the climate change will lead to extreme weather events like floods
and droughts in future. Pakistan is facing a number of climate change related threats
including rapid glacier melting, sea level rise, energy crisis and water and food
security (Ministry of Environment, 2011). Some other climate change issues include
loss of biodiversity, deforestation and ecosystem damage. The adverse health effects
caused by extreme changes in temperature and rainfall include heat strokes,
pneumonia, malaria (Planning Commission, 2010) dengue fever and other vector-
borne diseases (Ministry of Climate Change, 2012).
During last forty years, the Northern Pakistan has experienced an average
temperature rise of 1.5oC, whereas, it was recorded as 0.76oC in other areas
(Chaudhry et al., 2009). During 2001-2010, the average temperature in Northern
Areas of Pakistan increased by 1.3oC whereas temperature rise in the country on
average was 0.6oC (Rasul et al., 2008). Furthermore, the intensity and frequency of
heat waves have also been increased in the Himalayas, Karakoram and Hindukush
Mountain (Rasul et al., 2008). The water source in South Asian region is coming from
the glaciers in Himalayas and the Tibetan Plateau. Glacier melting will lead to an
irreversible water scarcity in the region (Mathez, 2009). There has been about four
times increase in severity of weather and storms in the South Asian region during past
three decades (Mathez, 2009).
Monsoon in Asian region has also been affected greatly by ‘Atmospheric
Brown Clouds’ and other pollutants coming from anthropogenic sources (Ramanathan
et al., 2007). These brown clouds also cause global warming leading to glacial
melting in Himalayas. This glacier melting is posing high risk to water availability in
future (Ramanathan et al., 2007). Summer monsoon in Asian region provides the
rainfall required for sustainability (Annamalai et al., 1999). It is evident that the dry
summers in eastern Mediterranian region and arid regions of North Africa are a result
of Asian summer monsoon (Rodwell, 1997; Hoskins, 1996). It is very important to
predict the variability in monsoon which would be significant for climate research and
future projections.
19
1.6 Climate Modeling
Long-range transport is another factor affecting the concentration of pollutants
in a region (Ohara, 2011). Air parcel back trajectories identify the actual source of
pollution which may be located in a far-off region (Dutkiewicz et al., 1987).
Backward and forward trajectories are very useful tool to trace the local and regional
transport of tropospheric ozone (Jiang et al., 2003). The climate models make future
projections of climate change; however, these models have practical limitations and
some uncertainties (Mathez, 2009). Multiple simulations by using the same model
may provide authentic information on natural variability and external elements
contributing towards climate change (Mathez, 2009). An important and more common
way to measure global warming is to measure the average increase in temperature
during a century and future projections (Mathez, 2009).
The variability of ozone concentration in the troposphere has been explained
by various global and regional scale chemical transport models. Ozone’s
concentration strongly depends on availability of precursor gases i.e., NOx, Non-
methane Hydrocarbons, carbon monoxide and meteorological conditions favouring its
formations in the troposphere (Delcloo and Backer, 2008).
Modeling studies have been carried out to examine the effects of climate
change more specifically on the regional ozone air quality by assuming the constant
primary emission into the atmosphere. Langner et al. (2005) examined the changes in
the accumulated ozone concentration from present to 2070 in Europe using regional
climate model driven by two different general circulation models (GCMs). It was
found that there was an increase in ozone concentration in central and southern
Europe, while a decreasing trend in the ozone concentration was observed over
Northern Europe affecting the regional pattern of precipitation.
It has been reported through regional model simulations that global warming
may lead to increase in concentration of aerosols as a result of increased formation of
aerosol precursors. Aerosol concentration is also expected to be influenced by
perturbations of precipitation frequencies and patterns (Aw and Kleeman, 2003). It is,
20
therefore, very important to make predictions and future projections about the impact
of air quality on climate change.
1.7 Significance of the Research Work
This dissertation is compilation of three research papers based on my Ph.D.
research work. The proposed research project focuses primarily on the assessment of
the sensitivity of air quality to climate change. The research work analyzes the
complex role of global change processes in the ambient air quality of Pakistan.
Ambient concentrations of criteria pollutants i.e., ozone (O3), oxides of nitrogen
(NOx), sulfur dioxide (SO2), non-methane hydrocarbons (NMHC), Total
Hydrocarbons (THC), Carbon Monoxide (CO) and Particulate Matter (PM10 and
PM2.5) have been analyzed in an effort to characterize air pollution in the urban
environment of Pakistan. This will be coupled with meteorological measurements to
gain an insight in the diurnal and seasonal variations of these pollutants. For this
purpose, Weather Research and Forecasting Model (WRF) has been used. WRF
model will be helpful in determining the mesoscale meteorological factors involved in
the variation of air quality and in response to convection. Forward and back
trajectories and ratio analysis; coupled with delta pollutant analysis will offer insight
into the origin, source, and magnitude of pollutants formed within the urban
environment.
This research work was aimed to find out the current scenario of air pollution
in the urban environment of Pakistan. It focused on seasonal variation of fine
particulate matter (PM2.5) in major cities of Pakistan and the effect of meteorological
conditions on the average mass concentration of PM2.5. The research work analyzed
the background concentration of ozone in Islamabad and the role of anthropogenic
precursor gases in its build up. A peculiar aspect of this research work is to calculate
the backward-trajectories in order to track the possible emission sources in other
regions, other than the local sources, responsible for ozone and PM2.5 pollution within
Islamabad city. Furthermore, synoptic analysis has been conducted for meteorological
condition using WRF model.
21
Understanding the nature and extent of climate change is a vital and important
area of research. It is also a great challenge for the modern society to cope with the
current air quality issues and the climate change. This study will enable to make
future projections on air quality and variability and intensity of weather patterns in
order to assess the impacts of climate change on Pakistan through simulations using
GCMs and other models. Although, substantial research is being done in the field of
air quality in Pakistan, there is insufficient research in projecting the impacts of
climate change and developing the adequate adaptation and mitigation options.
Research in the area of climate change would be helpful in promoting innovation to
address the environmental challenges faced by the country. Air quality and global and
regional climate models simulations can be used by the government authorities to
develop policies. Prediction of the impacts of climate change on air pollution is quite
complex and this area needs to be studied with strong emphasis on the future scenario
of climate change so that the control measures may be adopted and Environmental
Protection Act and rules and regulations made thereunder may be implemented.
1.8 Objectives
Main objectives of this research work are given below:
• Analysis of seasonal and diurnal variation of fine particulate matter in major
cities of Pakistan and assessment of effect of meteorological factors on the
atmospheric concentrations of PM2.5 and tropospheric ozone
• Observational based analysis of criteria pollutants in urban environment of
Pakistan and correlation analysis of tropospheric ozone with its precursor
gases
• Back-Trajectory analysis using HYSPLIT model in order to find out the
possible sources of pollution and the transportation mechanism of air pollutant
depending on meteorology.
• Assessment of potential for possible effects of climate change on air quality in
this region by simulation of high pollution episodes by Weather Research and
Forecast Model
22
CHAPTER 2: METHODOLOGY
2.1. Description of Sampling Sites
The air quality monitoring was carried out in four major cities of Pakistan i.e.,
Islamabad, Lahore, Peshawar and Quetta. The sampling sites (circles) are mentioned
in Figure 2.1:
Figure 2.1. Physical map of Pakistan showing the sampling sites (circles) in
Islamabad, Lahore, Peshawar and Quetta
2.1.1. Islamabad
Islamabad is the Federal Capital of Pakistan and is located at 33°26′N 73°02′E
with Margalla Hills surrounding the city from two sides (Siddique et al., 2012). It has
a population of approximately 1.15 million inhabitants. The average elevation of
Islamabad is 457-610 metres with lot of variation having the highest elevation of 1604
meters. Islamabad has a semi-arid sub-tropical climate having warm to hot humid
summers with monsoon season and a cold winter. The total area of Islamabad is 906
Km2 with an urban area of 220.15 Km2 (Capital Development Authority, 2012). The
23
air monitoring station is located at Central Laboratory for Environmental Analysis
and Networking (CLEAN), Sector H-8/2, Islamabad (Figure 2.2).
Figure 2.2. Monitoring Site at Central Laboratory for Environmental Analysis and Networking (CLEAN), Pak-EPA
2.1.2. Lahore
Lahore is located at 31°32′N 74°22′E with a municipal area of 332 km2, which
has been extended to 1 000 km2 due to urbanization. Lahore is the second largest city
of Pakistan with a population of about 9.01 million (Bureau of Statistics, 2012). There
are approximately 2.7 million motor vehicles and 1986 industries in the city (Bureau
of Statistics, 2012). Vehicular and industrial emissions are the main sources of air
pollution in the city (Stone et al., 2010). Lahore City is located at an elevation of 217
meters. The city is characterized by hot semi-arid climate with monsoon season and
dry and warm winters. The monitoring site is located in Township, a typical
residential area of Lahore city (Figure 2.3).
24
Figure 2.3. Monitoring Site of Punjab-EPA, Lahore
2.1.3. Peshawar
Peshawar is the capital city of Khyber-Pakhtunkhwa (KP) province and lies on
the Iranian plateau at 34°01′N 71°35′E. It has a population of about 3.6 million. The
city has an area of 1 257 Km2 and is located at an elevation of 359 meter. Semi-arid
climate is a characteristic of Peshawar. The air monitoring station is installed at the
rooftop of KP-EPA building located at Khyber Road, Peshawar (Figure 2.4).
25
Figure 2.4. Monitoring Site of KP-EPA, Peshawar
2.1.4. Quetta Quetta is the provincial capital of the Balochistan Province. Quetta is a bowl
shaped valley with an elevation of 1 680 meters surrounded by mountain ranges with
peak height above 3 000 meters (Muhammad et al., 2006). Quetta has an area of 2 653
km2 with the population of about 1.4 million inhabitants (Muhammad et al., 2006).
Quetta has a semi-arid climate and it does not experience monsoon rainfalls. Air
monitoring station is installed at Balochistan-EPA located at Samungli Road, Quetta
(Figure 2.5).
26
Figure 2.5. Monitoring Site of Balochistan-EPA, Quetta
2.2. Experimental Methods
2.2.1. Data Collection The air quality data for this research work was obtained from Pakistan
Environmental Protection Agency (Pak-EPA). Hourly air quality monitoring data for
five years (2007-2011) was collected using automated fixed and mobile air
monitoring stations (see Figures 2.6 and 2.7) for ambient concentration of six major
pollutants. The air monitoring stations are equipped with ambient air quality
analyzers, combined wind vane, anemometer (Koshin Denki Kogyo Co, Ltd. Model
KVS 501), thermo hygrometer (Koshin Denki Kogyo Co, Ltd. Model HT-010), solar
radiation meter (Koshin Denki Kogyo Co, Ltd. Model SR-010) and data logging
system (Horiba, Ltd. Model Special).
Data Logging systems at Federal and each Provincial EPA retrieve the air
quality data from air monitoring stations through data processing software. The
ambient air quality parameters like carbon monoxide (CO), oxides of nitrogen (NOx
27
i.e., NO and NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5),
and hydrocarbons (total hydrocarbons, non-methane hydrocarbons and methane) were
determined using specific and prescribed analyzers in air monitoring stations. The
detail of data points used for all cities for calculation of diurnal profiles and regression
analysis is given in Table 2.1.
Figure 2.6. Fixed Automated Air Quality Monitoring Station at Central Laboratory of Environmental Analysis and Networking (CLEAN), Pak-EPA
(Source: JICA TC-EMS, 2010)
28
Figure 2.7. Analyzers for Ambient Air within the Automated Air Quality Monitoring
Station
In Pakistan, the 24-hour National Environmental Quality Standards (NEQS) for PM2.5
has been set at 40 µg m-3, and the annual and hourly average have been set at 25 µg
m-3. A revised 24-h limit of 35 µg m-3 and annual and hourly average of 15 µg m-3
have become effective from 1st January, 2013 (Pak-EPA, 2010). On the basis of
severity of health effects of PM2.5, World Health Organization has set the guideline of
25 µg m-3 as 24-hour mean and 10 µg m-3 as annual mean (WHO, 2006). The
permissible limit of ozone is 180 µg m-3 for 1-hour average. However, a revised
standard value of 130µg m-3 is now effective from January, 2013 (Pak-EPA, 2010).
World Health Organization has set the guideline value for ozone levels at 100 µg m-3
for an 8-hour daily average (WHO, 2006). The annual average standard value for SO2
is 80 µg m-3, however, the 24-hour average value is 120 µg m-3. The annual and 24-
(Source: JICA TC-EMS, 2010)
29
hour average standard value for NO is 40 µg m-3, however, the annual average
standard value for NO2 is 40 µg m-3 and 24-hour average value is 80 µg m-3. The 8-
hour standard limit for CO is 5 mg m-3 and 1-hour limit is 10 mg m-3 (Pak-EPA,
2010). In this study, the concentrations of ambient air pollutants have been compared
with the NEQS applicable before January, 2013.
Seasons have been specified as winter (December - February), spring (March -
May), summer (June - August) and fall (September - November). Seasonal average
has been calculated in order to find out the variation of PM2.5 mass concentration in
various seasons.
Details of air quality analyzers are as follows:
2.2.2. Ambient Particulate Monitor PM2.5 was measured by Dust Analyzer (Horiba Ltd; Model APDA-370) with 0~5
mg/m3 range through β-ray absorption method (ISO6349). Specifications of the
analyzer are given as follows (JICA, 2007):
Specifications Principle: Beta Ray attenuation
Application: PM10 (PM2.5 by using filter paper)
Range: 0-5mg/m3(Auto-ranging) within ±10% of indication
Dust Collection: Filtration System; Use a glass fiber filter paper roll for over 1
or half month running
Suction Flow Rate: 1m3/hr (Automatic flow control)
Measurement time: 1 hour
Analog output: 0-1 V
Digital output: RS232 serial port
Self check and Diagnostics: Diagnostic messages or signals, flow rate, mass
concentration, mechanical movement, date, time, pressure,
daily average, source radiation intensity
30
Working temperature range: 10-40oC
Power source: AC 220V, 1-ph. 50Hz
Measuring Principle The Horiba dust monitor (APDA-370) measures and records the concentration
of particulate matter in ambient air automatically by beta ray attenuation principle. In
this procedure, beta rays are emitted by Carbon-14 (14C) in order to provide a constant
source of high-energy electrons. Sensitive scintillation detector is used to determine a
zero reading. The tape with beta rays is moved to the sample nozzle where a specific
amount of dust is captured by a vacuum pump. When this sample is put between
detector and beta source, the concentration of particulate matter is measured by
attenuation of beta rays (Horiba, 2009(a)).
2.2.3. NOx Analyzer: Chemiluminescence (ISO7996) method was used to determine NOx, NO and
NO2 concentrations using ambient NOx monitor (Horiba Ltd; Model APNA-370) with
detection limit of 0.5ppb and range of 0~1ppm. Thermal converter in NOx monitor is
known to introduce error in accurate concentration determination of ambient NO2 and
NOx. Since all other reactive oxidized nitrogen compounds also get converted to NO
during thermal conversion, thus, we propose using NOy′ to denote NOx. NOy´ may be
used as a surrogate for ambient NOy (= NO + NO2, HONO + HNO3 + PAN + NO3 + -
----) concentration. Specifications of NOx analyzer are as follows (JICA, 2007):
Specifications Principle: Chemiluminescence
Application: NOx
Range: 0-1 ppm (Auto-ranging and manual each channel)
Low detection limit: 1ppb
Zero drift: 2ppb/day
Span drift: 2%FS/day
Working temperature range: 5-40oC
Power source: AC 220V, 1-ph. 50Hz
31
Measuring Principle When ozone (O3) is added to the sample gas containing nitrogen oxide (NOx),
a part of nitrogen monoxide (NO) in the sample gas is oxidized to nitrogen dioxide
(NO2). Some concentration of NO2 is in the excited state (NO2*) and emits light in the
de-excitation state. This phenomenon of light emission is called chemiluminescence.
NO + O3 → NO2* + O2
NO2* → NO2 + hv
This reaction is extremely fast and involves only NO — affected little by the
other co-existent gases. When NO concentration is low, the intensity of light produced
is proportional to the NO concentration. The method using this reaction to measure
NO concentration is known as the chemiluminescence method (CLD method). In
APNA-370, sample is moved into the analyzer separately in two ways. At one side,
NOx concentration is determined by reducing NO2 to NO with a NOx converter; the
other is used for NO concentration measurement directly.
The gases are moved to the reference gas line, NO and NOx every 0.5 seconds
with solenoid valves, and are introduced to the reaction chamber in turn. On the other
hand, the open air is separately sucked through the air filter, dried by a self-
reproducing-type silica gel dryer, and used to form ozone in an ozonizer. Then, the
generated ozone is introduced into the reaction chamber. In the reaction chamber, the
sample and ozone react, and the light emission involved in the reaction is detected by
the photodiode. This instrument calculates NO, NO2 and NOx concentrations from the
outputs obtained by the photodiode, which are proportional to the NOx and NO
concentrations, and outputs the results as continuous signals (Horiba, 2009(b)).
2.2.3. Ambient SO2 Monitor: Sulfur dioxide was measured by SO2 monitor (Horiba Ltd; Model APSA-370) with
detection limit of 1ppb, range of 0~0.5ppm through U.V. fluorescence method
(ISO10498). Specifications of SO2 monitor are as follows (JICA, 2007):
Specifications: Principle: U.V. Fluorescence
Application: SO2 in ambient air
32
Range: 0-0.5 ppm (Auto-ranging)
Low detection limit: 1ppb
Zero drift: 2ppb/day
Span drift: ±2%FS/day
Sample gas flow rate: 0.7 litre/min.
Suction Flow Rate: 1m3/hr (Automatic flow control)
Measurement time: 1 hour
Working temperature
range:
5-40oC
Power source: AC 220V, 1-ph. 50Hz
Measurement Principle Sulfur dioxide emits light at various wavelengths i.e., ≤320 nm, range: 240 nm
to 420 nm due to irradiation with ultraviolet rays. This measuring principle is called
the fluorescence method as it determines SO2 through intensity of fluorescence light.
It measures SO2 with minimum influence of moisture. It does not require any
supplemental gas and gives linear output. When excitation light is irradiated and
absorbed:
I = I0e-aLx ...............(1)
Where:
I: intensity of the excitation light that passes through the cell
I0: initial intensity of the excitation light
a: absorption coefficient for the excitation light
L: cell length
Therefore, the amount of the excitation light absorbed in the cell, ∆I, is:
∆I = I0 − I = I0 (1 − e-aLx) ........(2)
33
The number of the SO2 excited in Process [I], SO2*, is proportional to the above ∆I.
[SO2*] = ∆I/hν1 ...(3)
SO2* produced by Process [I] is transferred from excited state to the ground state in
three ways.
• Fluorescence Process:
• Dissociation Process:
• Quenching Process:
These three processes lead to transferring the sulfur dioxide from excited state into
ground state. Accordingly, the number of the SO2* that goes through the fluorescence
process is:
Therefore, the fluorescence intensity detected with the photomultiplier is
expressed by the following equation, using the geometric constant of the cell, G:
34
If the SO2 concentration is low (1000 ppm or lower), the following equality is true
and the fluorescence intensity is proportional to the SO2 concentration, x (Horiba,
2009(c)):
2.2.4. Ambient CO Monitor: Ambient CO Monitor (Horiba Ltd; Model APMA-370), using non-dispersive
infrared-ray method (ISO4224) with detection limit of 0.1ppm and measuring range
of 0~50 ppm, was used to measure CO ambient concentration. The specifications of
the equipment are given as follows (JICA, 2007):
Specifications Principle: Non-dispersive infrared ray
Application: CO
Range: 0~50 ppm (auto-ranging and manual)
Low detection limit: 0.1ppm
Zero drift: 0.3ppm/day
35
Span drift: ±2% FS/day
Power source: AC 220V, 1-ph. 50Hz
Measuring Principle In CO monitor, carbon monoxide is determined by modulation effect caused
by absorption of infrared radiation when zero gas and sample are moved to its cell.
The detector’s output becomes zero due to which there is no zero drift. The output
starts increasing with increase in the measured gas concentration. APMA-370 CO
monitor provides the detections more accurately as there is no interference component
(Horiba, 2009(d)).
2.2.5. Ambient O3 Monitor Ambient Ozone Monitor (Horiba Ltd; Model APOA-370) with detection limit of 0.5
ppb, range of 0~1ppm and working on the principle of UV photometry method was
used to determine ozone concentration in ambient air. The specifications of O3
monitor are mentioned below (JICA, 2007):
Specifications Principle: U.V. Photometry
Range: 0~1 ppm (auto-ranging and manual)
Low detection limit: 0.5ppb
Zero drift: 2ppb/day
Span drift: 2% FS/day
Power source: AC 220V, 1-ph. 50Hz
Measuring Principle The ultraviolet absorption method is based on ozone's characteristic of
absorbing ultraviolet rays of specific wavelength. In this analysis method, the sample
gas which has passed through the filter is divided into two flow paths. The sample gas
in one path is introduced to the de-ozonizer, where its ozone is eliminated, and then
sent to the cell as “reference gas.” The sample gas in the other path is sent to the cell
directly, as “sample gas,” by switching a solenoid valve. The measurement cell is
exposed to direct radiation by a low-pressure mercury lamp which generates
36
ultraviolet rays with central wavelength of 253.7 nm, and a detector, which involving
a photodiode and electric system to obtain electric signals, measures ultraviolet
absorption by ozone.
The “sample gas” and the “reference gas” are sent to the cell alternately,
switched by 1 Hz with the solenoid valve. The deference in ozone content between
the reference gas and the sample gas can be obtained from the deference in the
measured ultraviolet absorption (Horiba, 2009(e)).
2.2.6. Ambient Hydrocarbon Monitor Ambient Hydrocarbon (HC) monitor was used to measure the ambient
concentrations of non-methane hydrocarbons (NMHCs), methane (CH4) and total
hydrocarbons (THC). Basic specifications of hydrocarbon monitor are as follows
(JICA, 2007):
Specifications (1) NMHC-THC Monitor
Principle: Converter oven
Application: Hydrocarbons
Detector: Hydrogen flame ionization
Range: 0~50 ppmC (auto-ranging and manual)
Low detection limit: 0.1ppmC
Zero drift: 0.5ppmC/day
Span drift: 1.0ppmC/day
Analog output: 2 signals (methane, Non-methane hydrocarbon)
Power source: AC 220V, 1-ph. 50Hz
(2) Hydrogen Generator
Application: For hydrogen gas supply to NMHC-THC monitor
Principle: Electrolysis of pure water (no use of any liquid caustic
electrolytes)
Gas purity: H2 purity more than 99.99%
37
Flow rate: 90 ml/min. (STP)
Power source: AC 220V, 1-ph. 50Hz
(3) Built-in Zero Gas Generator
Application: Supply of zero-free HC (for FID unit)
Flow rate: 100cc/min.
Pressure: 0.3 bar
Measuring Principle Hydrocarbon produces a high temperature flame, when it becomes in contact
with the hydrogen flame. The produced energy ionizes the hydrocarbon molecules at
the tip of nozzle. Direct current voltage between two electrodes opposite to each other
produces an ion current which is proportional to the carbon number of the ionized
hydrocarbon. When this ion current is passed through high resistance, it is converted
into voltage and then the concentration of total hydrocarbons is measured. In APHA-
370, the sample is used to measure the concentration of CH4 and THC separately. The
concentration of non-methane hydrocarbon (NMHCs) is detected by subtracting CH4
from THC (Horiba, 2009(f)).
2.2.7. Combined Wind Vane and Anemometer Anemometer measures the wind speed, whereas, the wind vane is used to determine
the direction of winds. The detail of the equipments is given below (JICA, 2007):
Specifications (1) Wind Direction Sensor
Type: Wind Vane (no heater)
Measuring Method: Opto-electronic transducer
Measuring range: 0 to 360o
Starting threshold: 0.5 m/s
Accuracy: Within ± 3o
Operating temperature range: -20 to 60 degC
(2) Wind Speed Sensor
38
Type: Propeller
Measuring Method: Opto-electronic transducer
Measuring range: 0.5 to 90 m/s
Starting threshold: 0.5 m/s
Accuracy: Within ± 3%F.S.
Operating temperature range: -20 to 60 degC
Thermo-Hygrometer
Specifications (1) Temperature Sensor
Materials: Pt resistance
Measuring range: -20 to 80 degC
Accuracy (at 0degC): Within ± 0.5 degC
(1) Humidity Sensor
Measuring Method: Thin film capacitor
Measuring range: 0 to 100% RH
Accuracy (at 20degC): Within ± 3% RH
Solar Radiation Meter
Specifications Method: Black-carbon thermopile
Spectral range: 400 to 2800 nm
Sensitivity: 7 mV/kw/m2
Operating temperature range: -20 to 60 degC
2.3. Synoptic Analysis for PM2.5 High Episodes: Synoptic analysis of high PM2.5 episodes in four cities of Pakistan was
conducted using reanalysis data of National Center for Environmental Prediction
(NCEP) / National Center for Atmospheric Research (NCAR). The data include air
temperature, U- and V-wind components, precipitation and relative humidity. Grid
39
Analysis and Display System (GrADS) was used to graphically display the
meteorological pattern during high PM2.5 episodes at four monitoring sites. Various
plots were developed using wind vectors, shaded contours and smoothed contours.
2.4. Back Trajectory Modeling Backward air trajectories were generated by using the Hybrid-Single Particle
Lagrangian Integrated Trajectory (HYSPLIT) model which has been developed by the
National Oceanic and Atmospheric Administration’s (NOAA) Air Resources
Laboratory (ARL). Archived three-dimensional meteorological data is used by
HYSPLIT model to compute the trajectories. Gridded Meteorological Data Archives
from Global Data Assimilation System (GDAS) of National Center for Environmental
Prediction (NCEP) / National Center for Atmospheric Research (NCAR) was used to
calculate the back trajectories. The trajectories were computed for the heights of
500m AGL, 1000m AGL and1500m AGL for a period of 24 hours. Label interval was
set to be six hours to track the path of trajectory. Mixed layer depth was also
determined for each back trajectory. The back trajectories were calculated for
Pakistan with a buffer zone including some part of China, India, Afghanistan, Iran and
Arabian Sea.
2.5. Weather Research and Forecasting (WRF) Model Simulations Weather Research and Forecasting (WRF) Model simulation was run for
synoptic analysis of Islamabad city during selected high ozone episodes. The
simulation was run over 18-Km resolution with 27 vertical levels, the lowest of which
was 1013hPa. The 18-Km grid covers the Capital Territory of Islamabad. Six-hourly
National Center for Environmental Prediction (NCEP) / National Center for
Atmospheric Research (NCAR) Final (FNL) Operational Global Analysis gridded
data (0.5o ×0.5o) was used as boundary and initial conditions for WRF simulations.
Default parameterization scheme has been used to run the WRF model. The
simulations for two episodes begin at 00:00 UTC and end at 18:00 UTC. 6:00 UTC
input was set for synoptic analysis of daytime, whereas 18:00 UTC was set as start
time for analysis of night-time meteorology during high ozone episodes.
40
CHAPTER 3: RESULTS AND DISCUSSION
SECTION I: ANALYSIS OF FINE PARTICULATE MATTER (PM2.5) IN URBAN AREAS OF PAKISTAN: AN
OBSERVATIONAL-BASED ANALYSIS
3.1. Spatial and Temporal Variation of PM2.5
Spatial and temporal variation in mass concentration of PM2.5 in different cities
gives an insight of the factors affecting the pollution levels with respect to
geographical location and time. Figure 3.1 provides the average annual and seasonal
PM2.5 mass concentration in Islamabad during 2007-2011. In Islamabad, average
annual PM2.5 mass concentration is 81.1±48.4 µg m-3, 93.0±49.9 µg m-3, 47.8±33.2 µg
m-3, 79.0±49.2 µg m-3 and 66.1±52.1 µg m-3 during 2007-2011 respectively. The
highest hourly average concentrations were observed as 303 µg m-3 during December
2007, 495.0 µg m-3 during November 2008, 259.8 µg m-3 during September 2009,
456.0 µg m-3 during October 2010 and 379.0 µg m-3 during January 2011. Main
sources of air pollution in Islamabad are rapid urbanization, vehicular and industrial
emissions, construction activities, emissions from brick kilns located on the outskirts
of Islamabad (Qadir et al., 2012; Siddique et al., 2012) and forest fires in the Margalla
Hills. There were about eighty wildfire incidents in the Margalla Hills during summer
months of 2006-2010 (The Express Tribune, 2011) contributing to the PM2.5 pollution
during summer months. Some dust storms also contributed to the PM2.5 burden in
Islamabad city during the monitoring period. A dust storm of 148 Km h-1 had hit
Islamabad on July 14, 2007 (Pakistan Weather Portal, 2013) and another storm with
an intensity of 130 Km h-1 struck the city on June 23, 2010 (GEO Pakistan, 2010).
Advection of air pollution from nearby city Rawalpindi also increases the pollution
level in Islamabad. Biomass combustion for space heating in the suburban areas of
Islamabad increases the mass concentration of PM2.5 during winter season. Moreover,
Margalla Hills also restrict the dispersion of pollutants leading to buildup of pollutants
level in Islamabad.
41
Figure 3.1. Annual and Seasonal Average PM2.5 Mass Concentration (µg m-3) in
Islamabad during 2007-2011 (±1 standard deviation is also shown in the figure; No. of data points given above the bars)
Figure 3.2 gives a comparison of annual and seasonal PM2.5 mass concentration
among Islamabad, Lahore, Peshawar and Quetta during summer 2007-Spring 2008.
Data shows that Lahore is the most polluted city among all the urban environments
with the highest PM2.5 mass concentration in each of the seasons. Furthermore, the
average annual and seasonal PM2.5 mass concentrations in all the cities, during whole
year, remained above the Pakistan NEQS.
The annual average PM2.5 mass concentration in Islamabad has been observed to
be 81.2±47.4 µg m-3 and the highest seasonal average PM2.5 mass concentration for
Islamabad has been observed as 98.5 µg m-3 during spring 2008. Fuel burning for
space heating in surrounding rural areas is a source of sulfate particulates. In addition,
atmospheric dispersion of PM2.5 is decreased during winter season due to lower
mixing depth and lower wind speed. Winter fog is another phenomenon restricting the
dispersion of pollutants away from the city. Lower concentration of PM2.5 in summer
42
than in winter may also be explained by the heavy monsoon rainfall during the
months of July and August (Sadiq and Qureshi, 2010).
Figure 3.2. Comparison of Annual and Seasonal Average PM2.5 Mass Concentration (µg m-3) in Islamabad, Lahore, Peshawar and Quetta during Summer 2007-Spring
2008 (±1 standard deviation is also shown in the figure; No. of data points given above the bars)
The annual average PM2.5 mass concentration for Lahore was observed to be
118.3±79.1 µg m-3 and the highest seasonal averaged PM2.5 mass concentration of
150.5±87.9 µg m-3 was observed during Fall, 2007. Primary sources of PM2.5 in
Lahore are diesel emission, biomass burning, coal combustion, two-stroke vehicles
and industrial activities (Stone et al., 2010; Dutkiewicz, et al., 2009). Peak values of
PM2.5 in winter season may be attributed to the primary emissions from combustion-
related sources, which increase during winter season due to indoor space heating.
Lodhi et al. (2009) has also documented about four times higher PM2.5 mass
concentration during winter than those observed in other seasons. Mixing height of
about 250 m during night time and 1000 m during day time in winter season (Husain
et al., 2007) may be another factor for accumulation and increase in concentration of
pollutants within this area. Lower mixing height traps the pollutants within a
particular area restricting the dispersion of pollutants to other areas. Long-lasting
43
episodes of stagnation accompanied by fog in Lahore are quite favorable for increased
formation and accumulation of sulfate particulates (Biswas et al., 2008). Wet
deposition mechanism is more pronounced in Lahore during summer season (Sadiq
and Qureshi, 2010).
The annual average PM2.5 mass concentration for Peshawar was found to be
86.2±50.0 µg m-3 and the highest seasonal PM2.5 mass concentration observed during
fall, 2007 was 104.1±51.1 µg m-3. Vehicular and industrial emissions are the primary
sources for PM2.5 mass concentration in Peshawar (Khan et al., 2008). Moreover,
increased number of brick kilns situated in and around the city (Jan et al., 2013) has
led to high level of PM2.5 because these kilns use low quality of coal and tyres as fuel.
Wood burning during winter season for space heating has a great influence on the
ambient mass concentration of PM2.5 during winter season (Sandradewi et al., 2008).
High PM2.5 mass concentration during winter may be attributed to the atmospheric
stability under low temperature and lower inversion layer. Such conditions lead to
accumulation of PM2.5 which has a higher residence time as compared to PM10. The
scavenging effect of monsoon rainfall may also be considered a factor for less PM2.5
mass concentration in summer than in winter.
The annual average PM2.5 mass concentration in Quetta was found to be 63.3±52.0
µg m-3 and the highest seasonal average PM2.5 mass concentration was observed to be
72.7±55.2 µg m-3 during Fall, 2007. Major sources of air pollution in Quetta include
emissions from motor vehicles, increased usage of two-stroke vehicles, dairy farms,
diesel-driven heavy vehicles, stone crushing, coal-fired power plants, industrial
activities and brick kilns (Quetta District Government, 2011; Muhammad et al., 2006;
Faiz et al., 1996). The topography of Quetta is favorable for accumulation of PM2.5
and its precursor gases subsequently leading to photochemical formation of secondary
PM2.5 as well. Quetta remains engulfed by a thick layer of smog during winter season
(Quetta District Government, 2011). High winter PM2.5 mass concentrations are
largely due to the combination of meteorology and increased primary combustion
emissions from space heating. During summer months, low pressure prevails over
Quetta due to which dust storms generated in deserts of Iran are transported to Quetta
(Muhammad et al., 2006). Higher PM2.5 concentrations in Quetta during summer 2007
44
may also be attributed to dust storm which was struck on July 23, 2007 (Wikipedia,
2013).
All of these cities are under the influence of extensive anthropogenic activities
leading to high PM2.5 mass concentration. Higher PM2.5 mass concentration during the
Fall season is due to burning of agricultural residue in the surrounding areas during
the months of September, October and November (SUPARCO, 2009). PM2.5 has
higher mass concentrations during winter season in all the cities compared with the
summer season. In winter season, elevated levels may be associated with increased
coal and biomass burning for heating purposes within the cities and nearby rural
areas. Inversion layers suppress the vertical transport of pollutants which leads to
elevated levels of pollutants during winter season. The difference between winter and
summer PM2.5 mass concentrations seems to be due to the combination of increased
burning of fossil fuel i.e., coal for space heating purposes in winter season; and
meteorological conditions i.e., shallow planetary boundary layer (Husain et al., 2007),
low precipitation level in cities other than Quetta and comparatively stable conditions.
3.2. Diurnal Profile of PM2.5
3.2.1. Annual Averaged Diurnal Profile
Figure 3.3 shows the integrated average hourly diurnal profile of PM2.5 mass
concentration in Islamabad, Lahore, Peshawar and Quetta during 2007-2011. Diurnal
profile of all four cities follows a similar pattern of variation – in general the first peak
occurs between 7.00 a.m. to 10.00 a.m.; and the second maximum in PM2.5 mass
concentration occurs during 9:00 p.m. to 1:30 a.m. This diurnal pattern shows that
even the lowest levels are above the Pakistan hourly NEQS for PM2.5.
45
Figure 3.3. Integrated Average Diurnal Profile of PM2.5 Mass Concentration (µg m-3) in Islamabad, Lahore, Peshawar and Quetta for 2007-2011 (±1 standard deviation is
also shown in the figure)
Night-time high PM2.5 mass concentration is because of increased movement of
heavy duty vehicles in the city and the development of the night time inversion layer
(diurnal meteorological changes). During winter season, high night time concentration
of PM2.5 may also be attributed to the increased use of biomass burning (fuelwood)
for heating purposes. The early morning elevation in PM2.5 mass concentration is
owing to an increase of the traffic density.
3.2.2. Seasonal Averaged Diurnal Profile
The seasonally averaged diurnal profile has been determined for each city
separately in order to find out any variation in PM2.5 mass concentration during
different seasons. Figure 3.4(a) shows the seasonal averaged diurnal profile of
Islamabad. It has been observed that the winter season has the highest values followed
by fall, spring and summer. The level of PM2.5 starts increasing at 8:00 a.m. with
increase in the traffic movement. Later, the concentration goes down during 4:00 p.m.
to 8:00 p.m. which may be due to formation of other secondary pollutants by PM2.5.
Another reason for low concentration during afternoon might be breakdown of
temperature inversion layer which builds up again in evening leading to higher
46
ambient concentrations of PM2.5. The seasonally averaged diurnal profile shows that
the concentration of PM2.5 is highest during winter season in Islamabad. Nighttime
high mass concentration of PM2.5 during winter season is in agreement to the usage of
coal for heating purposes. The diurnal profile for all seasons also indicates the
contribution of vehicular emissions towards the ambient PM2.5 mass concentration in
Islamabad during the study period.
Figure 3.4(a). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Islamabad
Figure 3.4(b) provides a seasonal diurnal profile of PM2.5 for Lahore. Similar trend
of PM2.5 mass concentration as that in Islamabad has been observed in Lahore with
highest values in winter and lowest values during summer season. The average hourly
concentration of PM2.5 has been observed to be higher than the standard limit of 25 µg
m-3 round the clock. PM2.5 peaks have been seen during 8:00 a.m. to 2:00 p.m. and
8:00 p.m. to 2:00 a.m. Daytime high concentrations may be due to traffic rush hours
and midnight high values of PM2.5 may be attributed to heavy duty vehicles
contributing largely to PM2.5 concentration in the city. Furthermore, alarmingly higher
mass concentration of PM2.5 during winter season implies that the increased coal
combustion during night time for indoor heating and long-lasting fog play a major
47
role in formation and buildup of PM2.5 within the city. The winter inversion layer at
night time also restricts the dispersion of pollutants.
Figure 3.4(b). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Lahore
Figure 3.4(c) shows the seasonally averaged diurnal profile of PM2.5 for Peshawar
city. The diurnal profile shows that the PM2.5 mass concentration remains above the
hourly standard limit of PM2.5. The concentration has been observed to be higher
during 8:00 a.m. to 10:00 a.m. in morning and during midnight. Morning time is peak
hours for traffic movement and evening rush hours and heavy duty vehicles during
night lead to high values in midnight. In Peshawar, the high PM2.5 mass
concentrations have been observed during fall and winter months. The diurnal profile
shows that the peak values of PM2.5 have been found during winter season from 8:00
a.m. to 10:00 a.m. and from 18:00 hrs to 22:00 hrs. Whereas, high PM2.5 mass
concentration during rest of the time have been observed during fall season. As
observed for other two cities, winter PM2.5 concentrations are higher than the summer
season.
48
Figure 3.4(c). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in Peshawar
In general there is a bimodal distribution in the diurnal PM2.5 seasonal
variation. The level of PM2.5 concentration has a peak during the morning associated
with increase in the traffic movement (i.e. mobile emissions and dust entrainment);
and a second peak associated with rush hour traffic and reduction in the planetary
boundary layer height. Midnight high values of PM2.5 may be attributed to heavy duty
vehicles carrying goods into and through the city. The seasonally averaged diurnal
profile shows that the concentration of PM2.5 is highest during winter season in
Islamabad, Lahore, and Peshawar associated with usage of coal for heating purposes
and long-lasting fog.
Seasonal diurnal profile of PM2.5 in for Quetta has been shown in Figure
3.4(d). The diurnal profile of PM2.5 in Quetta is different from other cities’ PM2.5
pattern in a way that here the mass concentration of PM2.5 is not as higher than that of
other seasons. However, there are exceedances of standard limit of PM2.5. The
variation of PM2.5 concentration during the day and night is similar to other cities’
profile.
49
Figure 3.4(d): Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in
Quetta
The concentration of PM2.5 goes down at midnight and starts increasing at 6:00
a.m. and again goes down at about 10:00 a.m. In later hours, the PM2.5 concentration
starts increasing after 6:00 p.m. due to high traffic movement in evening. Quetta,
being at high altitude (1680 m, MSL), has increased PM2.5 mass concentration owing
to high usage of biomass burning (fuel wood combustion) for space heating purpose
in winter compared to other cities. However, meteorological conditions in Quetta vary
with precipitation in winter due to western disturbance and no summer monsoon
rainfall (Chaudhary, 1992). Winter rainfall may be considered as a factor contributing
towards lower PM2.5 mass concentration in winter than summer season. During
summer months, stagnation and dry weather conditions prevail in Quetta leading to
enhanced accumulation of PM2.5 and its precursor gases within the city. The dust
storms generated in the desert of Iran are another major contributor towards the
pollution in Quetta during summer (Muhammad et al., 2006).
50
3.2.3. Workday-Weekend Variation in Diurnal Profile
The diurnal profile of PM2.5 mass concentration during workdays and weekends
has been analyzed separately in order to find out any variation in concentrations due
to change in traffic movement. Figure 3.5 shows the diurnal profile of annual
averaged PM2.5 mass concentration during workdays and weekends in Islamabad,
Lahore, Peshawar and Quetta. Diurnal profile is almost similar for workdays and
weekends in Islamabad with little variation during noon and midnight. At noon, the
peak of PM2.5 mass concentration starts later during weekend, whereas, at midnight,
the concentration goes higher than workdays. Daytime mass concentration of PM2.5 in
Lahore remains higher during workdays than that on weekends. However, the
midnight PM2.5 mass concentration increases during weekends. Similar pattern of
PM2.5 diurnal profile is observed in Peshawar with daytime higher concentration
during workdays and nighttime higher values during weekends. Quetta has a different
scenario as it has higher PM2.5 mass concentration during workdays than that
observed during weekends throughout the day. The maximum of peaks during
workdays are pronounced during workdays when traffic density is higher than the
weekends. Midnight higher PM2.5 mass concentration during weekends is in
agreement to the late night outdoor activities during weekends.
51
Figure 3.5 Workday-Weekend Variation of PM2.5 Mass Concentration in
(a) Islamabad; (b) Lahore; (c) Peshawar; and (d) Quetta
3.3. Effect of Meteorology on PM2.5
The PM2.5 mass concentration in Islamabad, Lahore, Peshawar and Quetta has been
correlated with meteorological variables in order to find out any possible association
between ambient PM2.5 mass concentration and meteorology in these cities. The
available data during 2007-2011 for these cities has been used for regression analysis.
3.3.1. PM2.5 and Temperature
Figure 3.6(a) shows the seasonal correlation of PM2.5 with temperature in
Islamabad. The figure shows that PM2.5 has a negative correlation (p ≤ 0.01; r = -0.2)
52
with temperature in winter season and a positive association (p ≤ 0.01; r = 0.065) with
temperature during summer season. About 4% of the variance in PM2.5 in winter and
0.4% variance during summer can be explained by its linear relationship with
temperature. Figure 3.6(b) gives the relationship between PM2.5 and temperature in
Lahore. The figure shows that 26% of the variance in PM2.5 can be explained by
temperature in winter months. During winter, PM2.5 has a strong correlation with
temperature (p ≤ 0.01; r = -0.5). Figure 3.6(c) shows that PM2.5 has a negative
correlation (p ≤ 0.01; r = -0.1) with temperature in winter season and a positive
correlation (p ≤ 0.01; r = 0.1) during summer months. Negative correlation observed
between PM2.5 and temperature during winter in Islamabad, Lahore and Peshawar
suggests that thermal inversion layers and fog may play a major role in elevation of
PM2.5 levels. Positive correlation between PM2.5 and temperature may also be an
indication of increased agricultural and biogenic emissions of ammonia and oxides of
nitrogen (Tai et al., 2010). Similar positive correlation of PM2.5 and temperature
during summer and a negative correlation during winter season have been found by
Barmpadimos et al. (2012).
53
Figure 3.6 Effect of Temperature on PM2.5 Mass Concentration (µg m-3) during 2007-
2011 in (a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta
However, the overall PM2.5 mass concentration trend with temperature is negative
for Islamabad, Lahore, and Peshawar. Tiwari et al. (2013) have also reported a
negative correlation of PM2.5 with temperature which depends on composition of the
particulate matter. This is perhaps due to the semi-volatile components such as nitrate
and organics are expected to decrease as they shift from the particle phase to the gas
phase at higher temperature (Sheehan and Bowman, 2001; Aw and Kleeman, 2003;
Tsigaridis and Kanakidou, 2007; Dawson et al., 2007; Kleeman, 2008).
Figure 3.6(d) shows the relationship between PM2.5 and temperature in Quetta. The
regression analysis shows that about 4% of the variation in PM2.5 is associated with
54
temperature in winter season and 10% of the PM2.5 variation is associated with
temperature in summer months. Quetta, being a high elevation site, the association
between PM2.5 and temperature is unlike other cities. Here, PM2.5 is positively
correlated (r = 0.2) with temperature in winter season, whereas, these two variables
are negatively correlated (r = -0.3) in summer. The correlation between PM2.5 and
temperature is statistically significant (p ≤ 0.01) during both the seasons. Negative
correlation observed between PM2.5 and temperature during summer suggests that
there may be more abundance of nitrate particles which get converted from particle to
gas phase due to high summer temperatures (Dawson et al., 2007). The positive
correlation of PM2.5 with temperature during winter may be due to the fact that the
winter precipitation in the form of rainfall and snow works as a scavenger for PM2.5
and perhaps plays an important role in decrease of PM2.5 mass concentration with
decrease in temperature during winter months justifying the positive relationship
between these two variables. The regression analysis of both the variables shows that
the variables other than temperature i.e., precipitation, cloud cover, wind speed, wind
direction and topography also play a major role in PM2.5 mass concentration during
both the seasons as low fraction of PM2.5 is dependent on temperature.
3.3.2. PM2.5 and Solar Radiation
Figure 3.7(a) shows the correlation between PM2.5 and solar radiation in Islamabad.
The regression analysis provides the information that about 0.4% of variation of PM2.5
during summer is associated with solar radiation and 0.7% variation in PM2.5 during
summer months depends on solar radiation. There is a correlation of r = 0.06 between
PM2.5 and solar radiation in winter and a correlation of r = 0.1 during summer. Figure
3.7(b) shows that PM2.5 has a significant (p ≤ 0.05) correlation with solar radiation (r
= -0.24) during winter season in Lahore, however, it doe s not have a good association
(r = 0.03) with solar radiation during summer season. The regression line shows that
6% of the PM2.5 variation is associated with solar radiation in winter and a negligible
fraction of about 0.1% has dependence on solar radiation.
55
Figure 3.7 Effect of Solar Radiation on PM2.5 Mass Concentration (µg m-3) during 2007-2011 in
(a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta
Figure 3.7(c) shows that PM2.5 is negatively correlated (p ≤ 0.01; r = -0.2) with
solar radiation during winter season but there is a weak association (r = 0.06) between
PM2.5 and solar radiation during summer months. About 3% of the variation in PM2.5
in winter may be explained by its linear relationship with solar radiation and 0.3% of
PM2.5 variation during summer is associated with solar radiation which is almost
negligible. Figure 3.7(d) shows that there is positive correlation of PM2.5 and solar
radiation during both winter and summer in Quetta. PM2.5 has a statistically
insignificant correlation (r = 0.06) with solar radiation during winter season and a
good correlation (p ≤ 0.05; r = 0.2) during summer season with 3% of variation in
PM2.5 caused by solar radiation.
56
3.3.3. PM2.5 and Wind Speed
Wind speed and mixing depth have strong effect on particulate matter (Jacob and
Winner, 2009). High wind speed results in dispersion of PM2.5 mass concentration and
stagnation leads to accumulation of PM2.5 in a particular area. The stagnant conditions
caused by low wind speed i.e., high pressure systems deteriorate the ambient air
quality (Leibensperger et al., 2008; Liao et al., 2006). Figures 3.8(a), 3.8(b), 3.8(c)
and 3.8(d) show the effect of wind speed on level of PM2.5 in Islamabad, Lahore,
Peshawar and Quetta respectively. It has been observed that there is a statistically
significant (p ≤ 0.01) negative correlation between PM2.5 and wind speed during
winter and summer seasons in all the cities. In Islamabad, PM2.5 has a negative
correlation with wind speed in both the seasons (winter: r = -0.298 and summer: r = -
0.17). About 9% of the PM2.5 variation in winter and 3% of PM2.5 variation is
associated with the linear relationship of PM2.5 with wind speed in Islamabad. Lower
correlation between PM2.5 and wind speed shows the contribution of local emission
sources towards the ambient concentration of PM2.5. PM2.5 has a correlation of r = -
0.3 during winter months and a correlation of r = -0.239 in summer in Lahore. About
10% of PM2.5 variation in winter and about 6% of PM2.5 variation is due to wind
speed in Lahore city.
57
Figure 3.8 Effect of Wind Speed on PM2.5 Mass Concentration (µg m-3) during 2007-2011 in
a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta
In Peshawar, PM2.5 has a negative correlation of r = -0.3 with wind speed during
winter months with dependence of about 10% of PM2.5 variation on wind speed.
There is a correlation of r = -0.185 between PM2.5 and wind speed in summer season
and 3% of PM2.5 variation is associated with wind speed in the city. PM2.5 and wind
speed have a negative correlation of r = -0.13 in winter and a correlation of r = -0.25
in summer months in Quetta. About 1% of PM2.5 variation in winter and 6% variation
in summer is explained by its relationship with wind speed in Quetta. Correlation of
PM2.5 with wind speed is stronger in winter than in summer in all cities except Quetta.
It indicates the contribution of local sources to high PM2.5 mass concentration in
Quetta during winter season. As Quetta is situated in a valley, there is stagnation in
58
the air leading to accumulation of PM2.5 mass concentration. Islamabad and Peshawar
have also mountains which hinder the transport of PM2.5. DeGaetano and Doherty
(2004) showed a negative correlation of wind speed with fine particulate matter.
3.3.4. PM2.5 and Vapour Pressure
Figure 3.9(a) represents the correlation between PM2.5 and vapour pressure for
Islamabad. PM2.5 has a negative correlation with vapour pressure (p ≤ 0.01; r = -0.2)
during winter months, however, it has a positive correlation with vapour pressure (p ≤
0.01; r = 0.05) during summer season. In winter season, about 4% of PM2.5 variation
is associated with vapour pressure whereas, about 0.3% of PM2.5 variance is
associated with its linear relationship; with vapour pressure during summer season.
Figure 3.9(b) shows that there is a correlation of PM2.5 with vapour pressure during
both the seasons in Lahore. There is negative correlation (p ≤ 0.01; r = -0.23) between
PM2.5 and vapour pressure during winter season and a positive correlation (p ≤ 0.01; r
= 0.07) during summer months. Regressions analysis shows that about 5% of the
variation in PM2.5 mass concentration is associated with vapour pressure in winter,
whereas, only about 0.5% PM2.5 variation is caused by vapour pressure during
summer.
59
Figure 3.9(c) gives the correlation of PM2.5 and vapour pressure in Peshawar. PM2.5
has statistically significant (p ≤ 0.01) positive correlation with vapour pressure during
both the seasons i.e., r = 0.36 in winter and r = 0.22 in summer season. About 13% of
the variation in PM2.5 is related to its linear relationship with vapour pressure during
winter months and about 5% of PM2.5 variation is associated with vapour pressure in
summer season. Figure 3.9(d) shows that PM2.5 has a positive correlation (r = 0.05)
with vapour pressure during winter season, and a negative correlation (p ≤ 0.05; r = -
0.12) during summer season in Quetta. As winter months are more humid, the positive
correlation between these two variables during winter show that anthropogenic
emissions like nitrates and sulfates are contributing more towards the PM2.5 pollution
Figure 3.9 Effect of Vapour Pressure on PM2.5 Mass Concentration (µg m-3) during 2007-2011 in a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta
60
(Tai et al., 2010). The negative correlation of PM2.5 with vapour pressure shows the
contribution of dust particles and elemental and organic carbon to PM2.5 level during
summer months (Tai et al., 2010; Wise and Comrie, 2005). Regression analysis shows
that there is weak association of PM2.5 variation with vapour pressure i.e., 0.3% in
winter and 1% in summer.
3.4. Linear Regression Analysis
Linear regression analysis has been conducted to determine the meteorological
variables that explain the most variance in the overall data. Summary of linear
regression analysis is given in Table 3.1. The regression analysis provides the extent
to which meteorological conditions affect the concentration of PM2.5 in the
atmosphere. The regression analysis shows that the four meteorological variables
accounted for 20% variance in PM2.5 mass concentration in Islamabad. Meteorology
seems to affect the PM2.5 mass concentration in Islamabad, Lahore and Peshawar
more during winter than in summer season. The meteorological factors accounted for
approximately 19% variance in PM2.5 mass concentration in Lahore city. About 13%
variance in average PM2.5 concentration in Peshawar is explained by the
meteorological conditions. Annual average mass concentration of PM2.5 in Quetta
seems to be less affected (6%) by meteorology as compared to other cities. It has been
observed that the effect of meteorology on PM2.5 is more prominent during winter
season in Islamabad (21%), Lahore (20%) and Peshawar (18%), however, Quetta
(located at high elevation) city has a different scenario. Here, the Meteorological
parameters have a contribution of about 8% variance in PM2.5 mass concentration
during winter months, whereas, about 24% of variance in PM2.5 is explained by
meteorology during summer season.
61
Table 3.1. Linear Regression Analysis of PM2.5 and Meteorological Variables for
Islamabad, Lahore, Peshawar, and Quetta
City Season Correlation Coefficient (r) Variance Explained
Islamabad
Winter 0.457 21%
Summer 0.211 4%
Annual 0.447 20%
Lahore
Winter 0.453 20%
Summer 0.251 6%
Annual 0.432 19%
Peshawar
Winter 0.424 18%
Summer 0.372 14%
Annual 0.357 13%
Quetta
Winter 0.279 8%
Summer 0.489 24%
Annual 0.239 6%
3.5. Analysis of High PM2.5 Episodes
Two high PM2.5 episodes for each city during winter and summer seasons have
been selected for synoptic analysis. These episodes were analyzed as a function of
62
meteorology in order to assess the impact of climate on air quality. The description of
high PM2.5 episodes along with the synoptic analysis is given as follows:
3.5.1. Islamabad Winter High PM2.5 Episode (December 1-9, 2007)
For Islamabad, December 1-9, 2007 was selected as a high PM2.5 episode due to
very high concentrations throughout this period. The time-series of PM2.5 mass
concentration and temperature during this period is given in Figure 3.10(a). The
maximum concentration of PM2.5 was observed to be 303.33 µg m-3 on December 6,
2007 which is quite high. The average concentration 130.44 µg m-3 of PM2.5 during
the episode period remains above the standard limit of 25 µg m-3. The average
temperature was observed in the range of 3-21oC.
Figure 3.10(a): Time Series of PM2.5 Mass Concentration and Temperature in Islamabad during December 1-9, 2007
Figure 3.10(b) gives the diurnal profile of PM2.5 during December 1-9, 2007. The
averaged concentration of PM2.5 is much higher than the standard value of PM2.5
throughout the day. Peak values of PM2.5 have been observed during midnight and
63
mid-day. It is contrary to the seasonal averaged diurnal profile of Islamabad where the
PM2.5 concentration was not as high during mid-day.
Figure 3.10(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in
Islamabad during December 1-9, 2007
Figure 3.10(c) shows the wind speed and wind direction in Islamabad city during
high PM2.5 episode. Wind speed has been observed to be very low and there is no
advection of air in the city. It implies the contribution of local pollution sources.
Furthermore, very low wind speed leads to accumulation of pollutants within the city
during this period. Figure 3.10(d) shows the back trajectory for PM2.5 episode during
December 1-9, 2007 which indicates the emission sources in Western India and
Afghanistan as possible transboundary sources for this episode.
64
Figure 3.10(c): Average Wind Speed (m s-1; contours) and Wind Direction
(vectors) in Islamabad during December 1-9, 2007
65
Figure 3.10(d). Back Trajectory Analysis of High PM2.5 Episode in Islamabad during
December 1-9, 2007
66
3.5.2. Lahore High PM2.5 Episode in Winter (February 1-25, 2008)
February 1-25, 2008 was selected as high PM2.5 winter episode for Lahore. Figure
3.11(a) gives the time-series of PM2.5 mass concentration and temperature during this
period. The maximum concentration of PM2.5 has been observed on February 1, 2001
to be too high i.e., 345 µg m-3. The average concentration 132.02 µg m-3 of PM2.5
during the episode period was also observed to be much higher than the standard
limit. The average temperature during February 1-25, 2008 was observed to be
28-48oC. Figure 3.11(b) shows the diurnal profile of PM2.5 during this period. The
mass concentration of PM2.5 remained too high throughout the day with values going
down during 10:00 a.m. to 4:00 p.m. After 4:00 p.m., the PM2.5 mass concentration
goes up again reaching its peak at 9:00 p.m.
Figure 3.11(a): Time Series of PM2.5 Mass Concentration and Temperature in
Lahore during February 1-25, 2008
67
Figure 3.11(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore
during February 1-25, 2008
Figure 3.11(c): Average Wind Speed (m s-1; contours) and Wind Direction
(vectors) in Lahore during February 1-25, 2008
68
Average wind speed (Figure 3.11(c)) during high PM2.5 episode is observed
to be 1.5 m s-1 and no advection has been observed during this time. Figure
3.11(d) represents the back trajectory for high PM2.5 episode during February 1-25,
2008 in Lahore indicating the pollution sources being transported from western side.
Figure 3.11(d). Back Trajectory Analysis of High PM2.5 in Lahore during February 1-25, 2008
69
3.5.3. Peshawar High Winter PM2.5 Episode (December 1-22, 2007)
High PM2.5 episode in winter season for Peshawar was selected for duration of
December 1-22, 2007. Figure 3.12(a) gives the time-series of PM2.5 mass
concentration and temperature during this period. The maximum concentration of
PM2.5 in Peshawar has been observed to be 321.83 µg m-3 on December 8, 2007. The
average concentration of PM2.5 during this period observed as 118 µg m-3 was much
higher than the standard limit. The average temperature during December 1-22, 2007
was observed in the range of 5-22oC. Figure 3.12(b) shows the diurnal profile of
PM2.5 during this period. The mass concentration of PM2.5 remained too high
throughout the daytime with values going down at 11:00 a.m. and then starts
increasing at 4:00 p.m.
Figure 3.12(a): Time Series of PM2.5 Mass Concentration and Temperature in
Peshawar during December 1-22, 2007
70
Figure 3.12(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in
Peshawar during December 1-22, 2007
Average wind speed (Figure 3.12(c)) during high PM2.5 episode is observed to be
about 1.5 m s-1 which is quite low and there is no advection of air into Peshawar
during this time.
Figure 3.12(c): Average Wind Speed (m s-1; contours) and Wind Direction
(vectors) in Peshawar during December 1-22, 2007
71
Figure 3.12(d) shows the back trajectory for PM2.5 episode during December 1-22,
2007 in Peshawar. The back trajectory analysis shows that the local emission sources
as well as Western Punjab, India are responsible for this high PM2.5 episode in
Peshawar.
Figure 3.12(d). Back Trajectory Analysis of High PM2.5 in Peshawar during December 1-22, 2007
72
3.5.4. Quetta High PM2.5 Winter Episode (December 1-18, 2007):
December 1-18, 2007 was selected as high PM2.5 episode for Quetta during winter
season. Figure 3.13(a) shows the time series of PM2.5 and temperature during the
episode period. The maximum concentration of PM2.5 in Quetta reached 283 µg m-3 on
December 6, 2007 which is very high concentration. The average concentration of
68.67 µg m-3 during this period remained higher than the NEQS. The temperature in
Quetta during the episode period was in the range of -1 to 18oC. Figure 3.13(b) shows
the diurnal profile of PM2.5 mass concentration during this period. The mass
concentration of PM2.5 starts increasing at 6:00 a.m. due to increase in traffic
movement and then goes down at about 10:00 a.m. The other peak is seen from 10:00
p.m. to midnight.
Figure 3.13(a): Time Series of PM2.5 and Temperature in Quetta
during December 1-18, 2007
73
Figure 3.13(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta
during December 1-18, 2007
The wind vectors show that the northerly winds are advecting into the city,
however, wind speed is very low i.e., 1 m s-1 (Figure 3.13(c)). Figure 3.13(d)
represents the back trajectory for PM2.5 episode in Quetta during December, 2007. The
trajectory shows the contribution of possible emission sources in South-western India
and local sources located in Sindh province.
74
Figure 3.13(c): Average Wind Speed (m s-1; contours) and Wind Direction
(vectors) in Quetta during December 1-18, 2007
75
Figure 3.13(d). Back Trajectory Analysis of High PM2.5 in Quetta during December 1-18, 2007
3.5.5. Lahore High PM2.5 Episode in Summer (June 1-12, 2007)
June 1-12, 2007 was selected as high summer PM2.5 episode for Lahore. Figure
3.14(a) shows the time series of PM2.5 and temperature during this period. The
76
maximum concentration of PM2.5 was observed as 222.5 µg m-3 on June 12, 2007 and
the average concentration of 80.87 µg m-3 during this period also exceeded the NEQS.
The average temperature during summer high PM2.5 episode was observed to be in the
range of 28-49oC. Figure 3.14(b) shows the diurnal profile of PM2.5 mass
concentration during this period. The hourly average mass concentration of PM2.5
remained high with lower values observed during 2:00 p.m. to 4:00 p.m.
Figure 3.14(a): Time Series of PM2.5 and Temperature in Lahore
during June 1-12, 2007
77
Figure 3.14(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore
during June 1-12, 2007
Southerly winds are being advected into the city, however, due to very low wind
speed; there is no dispersion of pollutants (see Figure 3.14(c)). Figure 3.14(d)
represents the back trajectory for high PM2.5 episode during June 1-12, 2007 in
Lahore. The trajectory analysis shows that north-western wind patterns may be a
possible source for this pollution episode.
78
Figure 3.14(c): Average Wind Speed (m s-1; contours) and Wind Direction
(vectors) in Lahore during June 1-12, 2007
79
Figure 3.14(d). Back Trajectory Analysis of High PM2.5 in Lahore during June 1-12, 2007
80
3.5.6. Quetta High PM2.5 Summer Episode (August 13-19, 2007)
August 13-19, 2007 was selected as high PM2.5 episode for Quetta during summer
season. Figure 3.15(a) shows the time series of PM2.5 and temperature during the
episode period. The maximum concentration of PM2.5 in Quetta reached 210.5 µg m-3
on August 16, 2007 and the average concentration observed as 79.2 µg m-3 is above
the NEQS. The average temperature of Quetta during the episode period was in the
range 19-35oC. Figure 3.15(b) shows the diurnal profile of PM2.5 during this period.
Peak values of PM2.5 are observed during 9:00 a.m. to 11:00 p.m. and at midnight.
There is convection of southerly and north-westerly winds in the study area. The
wind speed has been observed to be 3 m s-1(Figure 3.15(c)).
Figure 3.15(a): Time Series of PM2.5 and Temperature in Quetta
during August 13-19, 2007
81
Figure 3.15(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta
during August 13-19, 2007
Figure 3.15(c): Average Wind Speed (m s-1; contours) and Wind Direction
(vectors) in Quetta during August 13-19, 2007
82
Figure 3.15(d) shows the back trajectory for PM2.5 episode in Quetta during August
13-19, 2007 which indicates that the long-range transport of emissions coming from
Afghanistan and Turkmenistan contribute towards this high PM2.5 episode during
summer season in Quetta.
Figure 3.15(d). Back Trajectory Analysis of High PM2.5 in Quetta during August 13-19, 2007
83
3.6. Conclusion
This study attempts to characterize the PM2.5 pollution in four major urban centers
in Pakistan. The average PM2.5 mass concentrations are significantly higher in Lahore
than the other three cities. All the four cities have average PM2.5 concentration higher
than the Pakistan NEQS. These high concentrations of PM2.5 may be attributed to
primary sources e.g. coal and fossil fuel combustion coupled with the meteorological
conditions controlling the formation of secondary PM2.5 and their dispersion within
the troposphere. Analysis for high pollution episodes conducted using the NOAA
HYSPLIT model indicates that air trajectories influencing Lahore, Islamabad,
Peshawar and Quetta commonly originate from western India, especially in summer
as part of the prevailing monsoon circulation and eastern Afghanistan. These source
areas (states of Gujarat, Rajasthan and Punjab) have high concentration of industrial
activity and are likely sources of enhanced PM2.5 concentration, in addition to the
local sources. High mass concentration in winter may be a result of increased local
coal combustion activities due to more usage of coal and biomass for heating
purposes. Furthermore, in low temperatures, nitrate may have been exchanged from
gas-phase into particles which suggests that the ratio of nitrates particulate formation
in winter to sulfate formation in summer is high. It may also indicate that the primary
NOx emissions are more than SO2 emissions in these cities indicating higher
contribution from vehicular emissions. Surface inversion layers and fog contribute to
a great extent to hinder the dispersion of PM2.5 in urban areas of Pakistan especially in
Lahore where winter fog is extended and at its maximum. Lower mixing height in
winter season may be another factor as it does not allow dispersion of pollutants for
prolonged low temperature periods. Significant daily variation has also been observed
in PM2.5 mass concentration with peaks in morning till noon and another peak with
maximum values from evening to midnight. Among meteorological factors,
temperature and wind speed show a negative correlation with PM2.5 indicating the
dispersion of pollutants with the wind and accumulation of pollutants at low
temperatures.
84
Particulate pollution has become an issue of great concern in Pakistan, and its
control is a challenge for regulatory agencies. There is a need to strictly enforce the
vehicular and industrial emission standards in order to control the elevated pollution
levels.
85
SECTION II: MEASUREMENTS AND ANALYSIS OF
AIR QUALITY IN ISLAMABAD, PAKISTAN
Ambient air quality data of Islamabad for five years (2007-2011) was analyzed for
determination of average concentration of representative six air pollutants. The hourly
data for each pollutant collected was analyzed for average annual, seasonal and
diurnal variation. Analysis of various pollutants has also been conducted in order to
find out the role of precursors, possible emission sources, meteorology, origin of air
masses (based on back-trajectory analysis), and background concentrations.
4.1 Meteorology
The climate of Islamabad has a semi-arid climate with warm to hot humid
summers followed by monsoon season and a cold winter. In general, May and June
are the hottest months with average high temperature of ~38oC (100.4oF) observed in
June. In winter season, the average low temperature of ~2oC (35.6oF) may be
observed in January. Fog occurs in Islamabad during the winter season. Monsoon
season brings heavy rainfall and thunderstorm during July-September. In Islamabad,
temperatures vary from cold to mild, routinely dropping below zero. In the hills
(Margalla Hills) there is sparse snowfall. The highest temperature recorded was 46.5
oC (115.7 oF) in June, while the lowest temperature was −4 oC (24.8 oF) in January.
On 23 July 2001, Islamabad received a record breaking 620 millimetres (24 in.) of
rain fell in just 10 hours. It was the heaviest rainfall in 24 hours in Islamabad and at
any locality in Pakistan during the past 100 years (Hameed, 2007).
4.2 Average Concentration of Pollutants The average concentration of pollutants in Islamabad computed for PM2.5, NO,
CO and O3 concentrations are presented in Figures 4.1, 4.2, 4.3 and 4.4. Since the CO
standard is either 1-hour or an 8-hour standard, and the ozone is 1-hour average;
Figures 4.5 and 4.6 provide the numbers of exceedances of the ambient concentrations
for carbon monoxide and ozone during 2007 - 2011.
86
Figure 4.1. Annual Averaged PM2.5 Mass Concentration in Islamabad during 2007-
2011
Figure 4.2. Annual Averaged Concentration of NO (µg m-3) in Islamabad during 2007-2011
87
Figure 4.3. Annual Averaged Concentration of CO (mg m-3) in Islamabad during
2007-2011
Figure 4.4. Annual Averaged Concentration of O3 (µg m-3) in Islamabad during
2007-2011
88
Figure 4.5. Number of Exceedances of Annual Average Concentration of
CO (mg m-3) in Islamabad during 2007-2011
Figure 4.6. Number of Exceedances of Annual Average Concentration of O3 (µg m-3)
in Islamabad during 2007-2011
The annual average mass concentration of PM2.5 exceeds the Pakistan’s
National Environmental Quality Standard (NEQS) of 25 µg m-3 in each year (2007-
89
2011). In Islamabad, the annual average PM2.5 mass concentration is 81.1±48.4 µg m-
3, 93.0±49.9 µg m-3, 47.8±33.2 µg m-3, 79.0±49.2 µg m-3, 66.1±52.1 µg m-3 during
2007 to 2011 respectively; and the highest hourly values observed were 303 µg m-3
during December 2007, 495.0 µg m-3 during November 2008, 259.8 µg m-3 during
September 2009, 456.0 µg m-3 during October 2010, and 379.0 µg m-3 during January
2011. Such high mass concentrations of PM2.5 may be attributed to primary sources
such as black carbon aerosols (Husain et al., 2007; Viidanoja et al., 2002), and
secondary formation (i.e. gas-to-particle conversion) also contribute to PM2.5 (Raja et.
al., 2010). High PM2.5 is associated with adverse human health effects (Petrovic et al.,
2000).
Annual mean concentration of NO is also higher than the NEQS of 40 µg m-3
during 2007-2010, indicating the contribution of vehicular NO emissions. The hourly
average concentration of carbon monoxide for all the years is below the NEQS of 10
mg m-3. On some occasions, the hourly average ozone concentration exceeds the
NEQS primarily during the day during summer months (e.g. number of exceedances
of ozone concentrations during 2007 to 2011 were 121, 277, 324, 107 and 462
respectively). Figure 4.7 and Figure 4.8 give the time-series representation of air
pollutants in Islamabad during 2007-2011.
Figure 4.7. Time Series of Ambient Concentrations of O3, NO, SO2, PM2.5 and CO in
Islamabad during 2007-2011
90
Figure 4.8. Time Series of Monthly Averaged Concentrations of O3, NO, SO2, PM2.5
and CO in Islamabad during 2007-2011
4.3. Correlation of Air Pollutants
Figure 4.9 shows the correlation of CO with PM2.5 during 2007 to 2011. As
diesel combustion (from heavy duty vehicles and electric generators) is considered to
be a major source of both carbon monoxide and particulate matter, the correlation
between PM2.5 and CO was used to determine the possibility of similar source for
these two pollutants. Figure 4.9 shows that PM2.5 is significantly correlated (r = 0.61;
p-value ≤ 0.01) with carbon monoxide. From this plot, it may be inferred that the
sources other than automobiles (i.e. electric generators) also contribute towards
primary and secondary PM2.5 in the troposphere (since CO is primarily emitted from
the automobiles).
91
Figure 4.9. Correlation between CO and PM2.5 ambient concentration
during 2007-2011
Both carbon monoxide and the nitrogen oxides have many anthropogenic
sources in common including mobile sources (i.e. automobiles) and point sources (i.e.
energy production). It is therefore interesting to examine the relationships of these
species in ambient air, especially in an urban environment where the photochemical
transformations, including removal mechanisms, may be negligible; and then check
these relationship against emission inventories. Mobile sources often have the
characteristic of high CO/NO ratios and low SO2/NO ratios; whereas, higher SO2/NO
ratios and lower CO/NO ratios are associated with point sources (energy production).
Based on ambient data, Figure 4.10 and 4.11 provides the relationship between CO
and NO, and between CO and reactive nitrogen species, NOy′, in Islamabad during
2007 to 2011. A linear regression of hourly average CO and NO, and CO and NOy′
was performed which shows a significant (p-value≤0.01) correlation between CO and
NO concentrations ([CO]=10.13[NO]+511.3; r2=0.76), and a significant (p-
value≤0.01) correlation between CO and NOy′ concentrations
([CO]=9.84[NOy′]+256.8; r2=0.78). From this ratio analysis, relative background
concentrations may be determined by examining the intercept of the regression lines.
The regression curves reveal a background CO concentration of ~300 to ~600 ppbv in
92
the Islamabad urban area. This is similar to Raleigh, NC, USA, urban site value of
470 ± 52 ppbv (Aneja et al., 1997); however, CO background concentration in New
Delhi, India, has been observed as approximately 1693 ppbv (Aneja et al., 2001).
Figure 4.10. Correlation between CO and NO in Islamabad during 2007-2011
Figure 4.11. Correlation between CO and NOy′ in Islamabad during 2007-2011
93
Moreover, relative source strengths like mobile sources versus point sources
may also be suggested by examining the slope of the regression lines, and compared
with emissions inventory. Klimont et al. (2013) and ECCAD (2014) have provided an
emissions inventory (developed for the year 2010) for CO, SO2, and NOx. Table 1
compares and contrasts the emissions from this inventory by examining the
relationship between ambient CO and NOx, and between ambient SO2 and NOx for
2007 to 2011 in Islamabad, Pakistan. It also compares and contrasts with CO and NOx
relationship observed in Denver, CO, US (Parrish et al., 1991); Boulder, CO, US
(Goldan et al., 1995); Raleigh, NC, US (Aneja et al., 1997); and New Delhi, India
(Aneja et al., 2001). Based on ratio analysis of CO and NOx, Parrish et al. (1991)
reported values of 8.4, 7.8, and 10.2 for mobile sources in the Eastern US,
Pennsylvania area, and Western US, respectively. Given the average ratio of about 10
(i.e., the slope of the regression line) in Islamabad, it appears that mobile sources
contribute more to the concentrations of CO and NOx than point sources.
Monthly averages of sulfur dioxide concentration (1 µg m-3 SO2 = 0.38 ppbv)
is plotted in Figure 4.12. Sulfur dioxide concentrations are below Pakistan’s 24-hour
average NEQS value of 120 µg m-3 during the measurement period. A linear
regression of hourly average SO2 and NO concentrations (Figure 4.13) was performed
([SO2]=0.01[NO]+1.73; r=0.4). The ratio analysis of SO2/NO for Islamabad (slope
~0.01) (Table 1) indicates that point sources are contributing to SO2 in the city; also
corroborated by the emissions inventory for Islamabad (Klimont et al., 2014;
ECCAD, 2014).
94
Table 4.1. Ratio Analysis based on average emissions and/or ambient data
Region CO/NOx SO2/NOx
Eastern US a, b
Mobiles Point Sources
4.3
8.4 0.95
0.94
0.05 1.8
Pennsylvania area a, c
Mobiles Point Sources
2.6
7.8 0.8
1.7
0.05 2.3
Western US a, d
Mobiles Point Sources
6.7
10.2 1.2
0.41
0.05 1.1
Denver Metropolitan area a, e
Mobiles Point Sources
7.3
10.5 0.18
0.19
0.05 0.44
Raleigh, NC f
New Delhi, India g
16.3
50
0.73
0.58
This study
Based on 2010 Emission Inventory h,i
Mobiles Point Sources
Based on Ambient Data
4.94 0.63
10
0.34 7.0
0.01
a. Parrish et al, JGR, 1991 b. East of 95.5oW Longitude, South of 45oN latitude c. 76.5o - 81oW Longitude, 39o – 42o N latitude d. West of 104oW Longitude, South of 49oN latitude e. 104o -105.5oW Longitude, 39.5-41oN latitude f. Aneja et al, Chemosphere, 1997 g. Aneja et al, Environment International, 2001 h. Klimont et al, Environmental Research Letters, 2013 i. ECCAD, Emissions of atmospheric Compounds & Compilation of Ancillary Data, 2014.
95
Figure 4.12: Monthly average of SO2 concentration for 2007, 2008, 2010, and 2011 (I denotes ±1SD)
Figure 4.13. Correlation between SO2 and NO in Islamabad during 2007-2011
96
Figure 4.14 provides the correlation between PM2.5 and NO in Islamabad. The
association between PM2.5 and NO is significantly positive (p-value≤0.01; r=0.5)
suggesting that there is a contribution of NO in secondary production of PM2.5. Other
precursors (e.g. SO2) and primary sources (e.g. diesel generators) also lead to the
PM2.5 burden in the city as well.
Figure 4.14. Correlation of PM2.5 and NO in Islamabad for the Period 2007-2011
From the correlation among pollutants like CO, NO, SO2, and PM2.5 (Figure
4.15), it may be inferred that the pollution measured in Islamabad is primary in nature
having more association amongst species with direct emissions. The correlation
between PM2.5 and CO concentrations is an indication of direct emissions, most likely
from transport sector and fresh emissions from the industrial areas within the city
(Figure 4.15a). The CO concentrations are also owing to the chemical conversion of
VOCs via photochemistry; and some fraction of the PM2.5 also originates from gas-to-
particle conversion of SO2 and NOx. For the NOx emissions from the transport sector,
the nitric oxide (NO) is greater than 90 percent of the emissions (Vallero, 2008) and
readily converts to nitrogen dioxide (NO2) in the presence of sunlight.
97
The strong correlation between NO and SO2 indicates the contribution of
direct emission sources, such as emissions from transportation, industries, generator
sets (diesel combustion), and power plants. In polluted environments, as in case of
Islamabad, CO reacts with hydroxyl radicals and subsequently with NO to form ozone
through complex series of photochemical reactions (Figure 4.15c). Both carbon
monoxide and the nitrogen oxides have many anthropogenic sources in common
including mobile sources (i.e. automobiles), local industries, and point sources (i.e.
energy production) (Figure 4.15d). Nitric oxide (NO) emissions are readily oxidized
to nitrogen dioxide (NO2) in the presence of sunlight. Its subsequent complex
reactions with either volatile organic compounds (VOCs') and/or methane (CH4) lead
to the formation of tropospheric ozone (Figures 4.15e and 4.15f). Major source of
VOCs is a combination of automotive exhaust owing to incomplete combustion in the
vehicles because of relaxed maintenance and even adulteration of the fuel; and
industries and generator sets in and around Islamabad.
Figure 4.15(a): Correlations between Measured Daily Averages
of CO and PM2.5 in Islamabad during 2007-2011
98
Figure 4.15(b): Correlations between Measured Daily Averages
of NO and SO2 in Islamabad during 2007-2011
Figure 4.15(c): Correlations between Measured Daily Averages
of CO and O3 in Islamabad during 2007-2011
99
Figure 4.15(d): Correlations between Measured Daily Averages
of CO and NOy′ in Islamabad during 2007-2011
Figure 4.15(e): Correlations between Measured Daily Averages
of NMHCs and O3 in Islamabad during 2007-2011
100
Figure 4.15(f): Correlations between Measured Daily Averages
of CH4 and O3 in Islamabad during 2007-2011
4.4. Photochemistry of Ozone Formation
In the troposphere, ozone is formed in presence of sunlight by the precursors
involving NOx, methane, CO and VOCs/volatile hydrocarbons. VOCs/volatile
hydrocarbons and carbon monoxide react with NO in the prescence of sunlight to
form NO2; which is photolyzed to produce ozone. The correlation of ozone with its
precursors has been determined in order to find out the possible source contributions.
The correlation has been done for the summer (June, July and August) months to
compare and contrast ozone production efficiency during the summer season (Aneja
et al., 1996). To account for maximum photochemical activity during the day, and the
degree of conversion of NO to the reservoir NOy′ species, the time for this correlation
has been set as 9:00 a.m. to 3:00 p.m. O3 is plotted against (NOy′ – NO)/ NOy′ in
Figure 4.16.
101
Figure 4.16. Variation of concentration of Ozone vs (NOy’-NO)/NOy’ in the summer
months for 2007-2011 during maximum photochemical activity of the day i.e., 9:00 a.m. to 3:00 p.m.
This plot represents the relationship between ozone and the degree of
conversion of NO to reservoir NOy′ species. It is observed that ozone increases with
increase in the degree of photochemical conversion of NO to reservoir NOy′ species.
Ozone concentration is expected to be low in fresh air masses becauses it is primarily
formed by the same photochemiocal process which leads to the formation of NOy′
species such as HNO2, HNO3, PAN etc. Thus, with an increase in the ratio (NOy′ –
NO)/ NOy′, there is a consequent increase in the ambient concentration of ozone.
Results of this study show that aged airmasses have higher ozone concentration i.e.,
increasing (NOy′ – NO)/ NOy′. An exponential fit of the data yields [O3] = 30.9
exp(0.77(NOy′-NO)/ NOy′) . The intercept of O3 is ~31ppbv which represents the
nominal regional background concentration of ozone in ambient air which is not
influenced by the direct emissions. The regional background O3 concentration for
Islamabad is, therefore, ~31ppbv i.e., air advecting into Islamabad contains ~31ppbv
of ozone. This is similar to the nominal local background O3 concentration of
~28ppbv for Southeastern United States i.e. Raleigh, North Carolina, USA (Aneja et
al., 2000).
102
4.5. Diurnal Variation of Pollutants
Figure 4.17(a) shows the diurnal profile of ozone along with its precursors in
order to assess the influence of precursor pollutants on its production within the
troposphere. Ozone precursors (NOx, hydrocarbons, VOCs, and CO) build up during
the morning rush hour, and the ozone concentration starts increasing with a peak
between 12:00 noon to 16:00. At night time, the concentration of ozone decreases (no
photochemical activity) and goes to a minimum; where as high concentration of NO
and NMHCs occurs. Both NO and NMHCs increase owing to a combination of
evening automobile rush hour and trucks carrying freight through the city in evening
and night-time, no formation of ozone at night, and collapse of the planetary boundary
layer. In the morning, NMHCs initiate reactions for photochemical production of
ozone resulting in their minimum concentration and an ozone peak during mid-
afternoon.
Figure 4.17(a). Diurnal profiles of ozone, nitric oxide, CO and non-methane
hydrocarbons (NMHCs)
103
Figure 4.17 (b) shows the diurnal variation of average ozone concentration
during the four seasons. The diurnal profile is similar in all the four seasons due to the
fact that the tropospheric ozone formation takes place at daytime in presence of
sunlight. The maximum ozone concentration occurs during summer season due to
high temperature and high solar intensity; and the minimum concentration levels were
observed during winter season. The lower formation of ozone in winter is because of
low temperature and low solar intensity. With the beginning of spring season, the
formation of ozone increases.
Figure 4.17(b). Seasonal and diurnal variation of averaged ozone concentration
during 2007-2011 (±1 standard deviation is also shown in the figure)
The lower formation of ozone in winter is because of low temperature and low
solar intensity. The lower formation of ozone in winter is because of low temperature
and low solar intensity. Another reason of low concentration of ozone during winter
season is that the long-range transport of precursor gases is limited in winter, which
also contributes to some extent to its lower formation rate. And with the beginning of
spring season, the formation of ozone is increased.
104
4.6. Effect of Meteorology on Air Pollutants
The formation of tropospheric ozone is strongly dependent on meteorological
conditions especially atmospheric temperature and solar radiation (NRC, 1991).
Figures 4.18(a) and 4.18(b) provide the relationship of ozone with temperature and
solar radiation. Ozone has been observed to be positively correlated with temperature
(p ≤ 0.01; r = 0.694) and solar radiation (p ≤ 0.01; r = 0.601). About 48% of variance
in ozone concentration during daytime is explained by temperature, whereas, solar
radiation affects about 36% of the variation in ozone concentration. The positive
correlation of ozone with temperature and solar radiation is due to their role in
photochemical formation of ozone. Similar relationship of ozone with temperature
and solar radiation has been reported by NRC (1991), and Jacob and Winner (2009).
Figure 4.18(a). Correlation of Ozone with Temperature during 2007-2011
at 9:00 a.m. – 3:00 p.m.
105
Figure 4.18(b). Correlation of Ozone with Solar Radiation during 2007-2011
at 9:00 a.m. – 3:00 p.m.
Figure 4.18(c). Correlation of PM2.5 with Temperature during 2007-2011
at 9:00 a.m. – 3:00 p.m.
106
Figure 4.18(d). Correlation of PM2.5 with Solar Radiation during 2007-2011
at 9:00 a.m. – 3:00 p.m.
Figures 4.18(c) and 4.18(d) represent the correlation of PM2.5 with temperature
in Islamabad. The figure shows that PM2.5 has a negative correlation (p ≤ 0.01; r = -
0.44) with temperature during 9:00 a.m. to 3:00 p.m. About 19% of variance in PM2.5
can be explained by its linear relationship with temperature. The regression analysis
of PM2.5 and solar radiation shows that about 13% of variation in ambient PM2.5
concentration in Islamabad during daytime is associated with solar radiation. There is
a statistically significant correlation of r = -0.36 between PM2.5 and solar radiation.
Tiwari et al (2013) has also reported a negative correlation of PM2.5 with temperature
which depends on composition of the particulate matter. This is perhaps due to the
semi-volatile components such as nitrate and organics are expected to decrease as
they shift from the particle phase to the gas phase at higher temperature (Sheehan and
Bowman, 2001; Aw and Kleeman, 2003; Tsigaridis and Kanakidou, 2007; Dawson et
al., 2007; Kleeman, 2008).
4.7. Linear Regression Analysis
Linear regression analysis has been conducted to determine the meteorological
variables that explain the most variance in the overall data. Summary of linear
regression analysis is given in Table 4.2. The regression analysis clearly explains the
107
extent to which meteorological conditions affect the concentration of PM2.5 in the
atmosphere.
Variable Season Statistical Analysis Weighted Variables
Wind Speed Temperature Vapour Pressure
Solar Radiation
O3
Annual
Correlation Coefficient (r) 0.792
0.346
0.717
0.131
0.622
Variance Explained (%) 63%
12%
49%
4%
40%
Winter
Correlation Coefficient (r) 0.748
0.59
0.654
-0.116
0.493
Variance Explained (%) 56%
35%
44%
0.1%
29%
Spring
Correlation Coefficient (r) 0.816
0.341
0.793
-0.176
0.636
Variance Explained (%) 67%
12%
65%
2%
46%
Summer
Correlation Coefficient (r) 0.763
0.362
0.727
-0.157
0.626
Variance Explained (%) 58%
13%
43%
0.7%
35%
Fall
Correlation Coefficient (r) 0.816
0.344
0.757
0.024
0.597
Variance Explained (%) 67%
12%
61%
3%
48%
Variable Season Statistical Analysis Weighted Variables
Wind Speed Temperature Vapour Pressure
Solar Radiation
O3
Annual
Correlation Coefficient (r) 0.792
0.346
0.717
0.131
0.622
Variance Explained (%) 63%
12%
49%
4%
40%
Winter
Correlation Coefficient (r) 0.748
0.59
0.654
-0.116
0.493
Variance Explained (%) 56%
35%
44%
0.1%
29%
Spring
Correlation Coefficient (r) 0.816
0.341
0.793
-0.176
0.636
Variance Explained (%) 67%
12%
65%
2%
46%
Summer
Correlation Coefficient (r) 0.763
0.362
0.727
-0.157
0.626
Variance Explained (%) 58%
13%
43%
0.7%
35%
Fall
Correlation Coefficient (r) 0.816
0.344
0.757
0.024
0.597
Variance Explained (%) 67%
12%
61%
3%
48%
Table 4.2. Linear Regression Analysis of Ozone and PM2.5 with Meteorological Variables
108
The regression analysis shows that the four meteorological variables
accounted for 20% variance in PM2.5 mass concentration in Islamabad. Meteorology
seems to affect the PM2.5 mass concentration in Islamabad, Lahore and Peshawar
more during winter than in summer season. The meteorological factors accounted for
approximately 19% variance in PM2.5 mass concentration in Lahore city. About 13%
of the variance in average PM2.5 concentration in Peshawar is explained by the
meteorological conditions. Annual average mass concentration of PM2.5 in Quetta
seems to be less affected (6%) by meteorology as compared to other cities. It has been
observed that the affect of meteorology on PM2.5 is more prominent during winter
season in Islamabad (21%), Lahore (20%) and Peshawar (18%), however, Quetta city
has a different scenario. Here, the Meteorological parameters have a contribution of
about 8% variance in PM2.5 mass concentration during winter months, whereas, about
24% of variance in PM2.5 is explained by meteorology during summer season.
4.8. Back Trajectory Analysis
The back trajectory analysis using the National Oceanic and Atmospheric
Administration (NOAA) HYSPLIT was conducted in order to study the atmospheric
transport of air pollutants and their precursors and to find out the potential source
regions for air pollution episodes in Islamabad during 2007-2011. Four pollution
episodes for PM2.5 and ozone (Figure 4.19 and Figure 4.20) were selected for back
trajectory analysis which show that the important source areas (during high pollution
episodes) reaching Islamabad are located in eastern Afghanistan and western India.
109
(a): Back Trajectory for High PM2.5 Episode during 1st – 30th November, 2007’
(b): Back Trajectory for High PM2.5 Episode during 1st – 30th January, 2011
(c): Two-days Back Trajectory for High O3 Episode during 19th – 22nd September, 2009’
(d): Three-days Back Trajectory for High O3 Episode during 10th – 13th July, 2011
Figure 4.19. Air Parcel Back Trajectories for PM2.5 and Ozone Episodes during 2007-2011
110
Figure 4.20. Air parcel 48-hour back trajectories analysis for some selected PM2.5 and Ozone high pollution episodes during 2007-2011
111
Major pollution sources from Afghanistan particularly Kabul and Jalalabad
include vehicular and industrial emissions, biomass burning, use of diesel electric
generators and burning of tyres. The source regions in Western India are located in
the states of Gujarat, Rajasthan and Punjab (i.e., Southeast of Islamabad) and are
known to have high concentration of industries and mechanized farming that are
sources of particulate and gaseous emissions. During winter, the monsoon flow
reverses and local sources of emissions in Islamabad due to burning coal and wood
are more important.
4.9. Conclusions
The ambient air quality for criteria pollutants has been characterized for
Islamabad, Pakistan, during 2007-2011. The annual and hourly average concentrations
show that the annual average concentrations of PM2.5 and NO are higher than the
Pakistan NEQS. Transportation is a major source of such high concentrations of NO.
The hourly average concentrations of ozone exceeds the NEQS primarily during the
summer season. Carbon monoxide and sulfur dioxide are within the safe limit.
Seasonal profile of ozone concentration shows that summer is the peak season for
photochemical production of ozone, while the winter season has the minimum
concentration of ozone amongst the four seasons. The back trajectory analysis using
the National Oceanic and Atmospheric Administration (NOAA) HYSPLIT show that
during summer months, important source areas of trajectories reaching Islamabad are
located in eastern Afghanistan and western India. The source regions in the Indian
states of Gujarat, Rajasthan and Punjab (i.e. Southeast of Islamabad) have high
concentration of industries and mechanized farming that are sources of particulate and
gaseous emissions.
This study reveals that the background concentration of carbon monoxide in
Islamabad (~300 to ~600 ppbv) is larger than Western US background CO
concentration (~200ppbv). The ratio of CO/NO (~10) indicates that the mobile
sources contribute predominantly to the ambient concentration of these compounds;
while the ratio of SO2/NO (~0.01) indicates that the point sources primarily contribute
to SO2 pollution within the city. The ratios of measured concentrations of [CO] to
112
[NO], and [SO2] to [NO] observed in Islamabad provide a test for emission
inventories. The ratios of these pollutants in the available Islamabad emission
inventories are consistent with ratios obtained from ambient values for these
pollutants.
Keeping in view the current air quality conditions in Islamabad, Pakistan, which are
degrading the atmospheric environmental conditions, there is an urgent need to
develop effective strategies for pollution control. There is also a need for regulatory
agency to enforce the emission standards for industries and motor vehicles in order to
meet the ambient air quality standards. Extensive spatial and temporal air quality
monitoring and modeling with an integrated assessment are significantly required in
developing comprehensive solution to the air quality concerns of Islamabad, Pakistan.
113
SECTION III: BACK TRAJECTORY ANALYSIS AND SIMULATION OF OZONE HIGH EPISODES BY WRF
MODEL IN ISLAMABAD, PAKISTAN
5.1. Ozone Episodes in Islamabad City Industrialization and rapid urbanization in Islamabad have led to increased
pollution within Islamabad city. It is very important to monitor the high ozone
episodes in order to track the photochemical smog in Islamabad. The high ozone
episodes were identified by analysis of hourly ozone concentrations and exceedances
than the standard limit of 66 ppbv. Ozone concentrations during these episodes have
been observed to be too high. High ozone episodes have been observed to be lasting
for many days which is quite alarming.
5.2. Back Trajectory Analysis
The air mass back trajectories identify the actual source of pollution which
may be located in a far-off region (Dutkiewicz et al., 1987). Back trajectories were
computed in order to identify the origin of air parcels carrying the air pollutants. The
trajectories were calculated at the altitudes of 500m, 1000m and 1500m AGL for
some of the ozone high episodes. Low-ending trajectories represent air parcels nearer
the ground level, and consequently nearer the ground-based samplers. It has been
observed that the trajectory heights are not constant during the observed days. High-
ending trajectories may represent more accurate boundary layer flow above the local
terrain. Trajectory heights are not constant throughout the trajectories’ duration and
vary significantly from the selected height. Back trajectories were calculated for
different durations so that a possible source origin may be identified. The back
trajectory analysis for high ozone episodes in Islamabad during 2007-2011 is as
follows:
114
Figure 5.1 shows the HYSPLIT back trajectory of air mass from Islamabad for the
ozone high episode during 27th August – 2nd September, 2007. The trajectory was run
for 96 hours so that a particular source of pollution may be identified. It is observed
that the air mass at 500m AGL was transported from South India. Whereas, the air
parcel at 1000m and 1500m AGL are originated from Iran and are passing through
Afghanistan. It implies that the emission sources in Afghanistan and Rajasthan, India
may be possible sources of high ozone in Islamabad during this period.
Figure 5.1. Back Trajectory Analysis of High Ozone Episode in Islamabad during 27th August – 2nd September, 2007
115
Another high ozone episode lasted for about a month in Islamabad on 7th – 19th
September, 2007 (Figure 5.2). The air mass is advecting from Afghanistan in the
westerly direction. The winds, originated from Uzbekistan and Tajikistan are
transported through the cities of Kabul, Gardez and Khost.
Figure 5.2. Back Trajectory Analysis of High Ozone Episode in Islamabad during September 7-19, 2007
116
Figure 5.3 shows the HYSPLIT back trajectory of air parcel from Islamabad during
September 25-27, 2007 as this period is characterized by very high ozone
concentrations. Total run time for back trajectory was 96 hours. The air masses at
500m and 1000m AGL altitudes have been observed to be coming from Afghanistan
through the cities of Jalalabad and Asadabad.
Figure 5.3. Back Trajectory Analysis of High Ozone Episode in Islamabad during September 25-27, 2007
117
Figure 5.4 shows the air mass transport during high ozone episode by HYSPLIT back
trajectory during October 12-21, 2007. Total run time for this back trajectory was 96
hours. The air parcel was transported from Afghanistan at 500m, 1000m and 1500m
altitudes showing contribution of transboundary air pollution affecting the air quality
of Islamabad city.
Figure 5.4. Back Trajectory Analysis of High Ozone Episode in Islamabad during October 12-21, 2007
118
The HYSPLIT back trajectory given in Figure 5.5 has been computed for ozone high
episode during 28th April – 1st May, 2008. The back trajectory was calculated for 48
hours prior to the episode. It is revealed from the trajectory that the air masses at all
altitudes have similar backward direction coming from china.
Figure 5.5. Back Trajectory Analysis of High Ozone Episode in Islamabad during
28th April – 1st May, 2008
119
The HYSPLIT back trajectory given in Figure 5.6 has been computed for ozone high
episode during 10th May – 1st June, 2008. Total run time for the trajectory is 96 hours.
The back trajectory reveals that the air masses during the episode are most likely
representative of local pollution source. However, there is indication of air mass
transport from Afghanistan and India also at 500m and 1000m AGL respectively.
Figure 5.6. Back Trajectory Analysis of High Ozone Episode in Islamabad during
10th May – 1st June, 2008
120
Figure 5.7 shows the HYSPLIT back trajectory of air mass from Islamabad during
high ozone episode of June 4-13, 2008. The back trajectory was run for four days. It
has been observed that the air mass at 500m and 1000m AGL height are originated
from Uzbekistan in north-west and then transported towards Pakistan through
Jalalabad, Afghanistan. The air parcel at 1500m AGL is transported through Ghazni,
Afghanistan.
Figure 5.7. Back Trajectory Analysis of High Ozone Episode in Islamabad
during June 4-13, 2008
121
The back trajectory for high ozone episode of June 20-24, 2008 is shown in Figure
5.8. Total run time for back trajectory was 120 hours. The air mass at 1000m and
1500 AGL altitudes are coming from Afghanistan. The air parcel at 500m AGL shows
the contribution of emission sources in west India towards high pollution in Islamabad
during this episode. The air mass at 1000m AGL has been observed to be transported
from the Arabian Sea, whereas, the air mass at 1500m AGL is coming from south-
west originated from Afghanistan.
Figure 5.8. Back Trajectory Analysis of High Ozone Episode in Islamabad during June 20-24, 2008
122
Figure 5.9 shows the back trajectory for high ozone episode on August 25-27, 2008.
The back trajectory was run for 96 hours. The air mass during the episode period is
originated from the Arabian Sea at 500m AGL and the air parcels at 1000m and
1500m AGL are transported from the southern parts of Pakistan. Stagnant conditions
also prevail before the high ozone episode as evident from the trajectory.
Figure 5.9. Back Trajectory Analysis of High Ozone Episode in Islamabad during
August 25-27, 2008
123
The back trajectory for a high ozone episode in Islamabad during May 13 - 21, 2009
is given in Figure 5.10. Total run time for back trajectory is 72 hours. It has been
observed that the air mass at 500m and 1000m AGL are originated in Iran and then
transported towards Islamabad through Afghanistan. However, the air parcel at
1500m shows long-range transport originating from Iraq and carrying the pollutants
on its way from Iran and Afghanistan.
Figure 5.10. Back Trajectory Analysis of High Ozone Episode in Islamabad during
May 13-21, 2009
124
The back trajectory for high ozone episode of May 6-31, 2009 is shown in Figure
5.11. Total run time for the trajectory is 72 hours. It has been revealed from the back
trajectory that the air mass at 500m AGL originates from Indian Punjab carrying coal-
fired power plants. There seems to be high stagnation at this altitude. The air mass at
1000m AGL is being transported from Iran through Afghanistan, whereas, the air
parcels at 1500m AGL show a long-range transport showing its origin from Turkey
and then being transported through Turkmenistan, Uzbekistan and Afghanistan.
Figure 5.11. Back Trajectory Analysis of High Ozone Episode in Islamabad during May 6-31, 2009
125
Figure 5.12 shows the back trajectory of high ozone episode occurred on August 7-9,
2009. The back trajectory was run for three days from the start of high ozone episode.
It is evident from the back trajectory that the air masses are originated from the
Arabian Sea.
Figure 5.12. Back Trajectory Analysis of High Ozone Episode in Islamabad
during August 7-9, 2009
126
Figure 5.13 shows the back trajectory for a high ozone episode in Islamabad during
August 22-25, 2009. Total run time for back trajectory is 96 hours. It is observed from
the back trajectory that the movement of air masses towards Islamabad is diverse with
respect of altitude. The lower altitude air masses at 500m AGL are coming from
Karachi in north-east. The air mass at 1000m AGL originates from Kazakhstan and is
transported through Uzbekistan and Afghanistan. However, the air parcels at higher
altitude of 1500m AGL is originated from Balochistan province of Pakistan in south-
west.
Figure 5.13. Back Trajectory Analysis of High Ozone Episode in Islamabad during August 22-25, 2009
127
The back trajectory for high ozone episode of August 27-30, 2009 is shown in Figure
5.14. Total run time for the back trajectory is 96 hours from the onset of high ozone
event. It is observed from the back trajectory that the high altitude air masses at
1500m AGL are originated from Turkmenistan crossing Afghanistan to reach
Islamabad. The air masses at 1000m AGL are coming from the South possibly
originated from the Arabian Sea. The air parcels at 500m AGL are originated from
Afghanistan.
Figure 5.14. Back Trajectory Analysis of High Ozone Episode in Islamabad during August 27-30, 2009
128
Figure 5.15 shows the back trajectory for a high ozone episode in Islamabad on 19th –
22nd September, 2009. Total run time for back trajectory is 96 hours. It is observed
from the back trajectory that the high altitude air masses at 1000m and 1500m AGL
are originated from Iran and are transported through Afghanistan. The air masses at
500m AGL are originated from Afghanistan.
Figure 5.15. Back Trajectory Analysis of High Ozone Episode in Islamabad during
September 19-22, 2009
129
Figure 5.16 shows the back trajectory for a high ozone episode in Islamabad during
11th – 13th June, 2010. Total run time for back trajectory is 120 hours. The back
trajectory shows stagnant conditions in Islamabad prior to the episode. The air masses
before this episode are originated from the Arabian Sea and then are transported
towards Islamabad passing through southern India.
Figure 5.16. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 11-13, 2010
130
The back trajectory for high ozone episode of June 19-23, 2010 is shown in Figure
5.17. Total run time for the back trajectory is 72 hours. The back trajectory shows that
the air mass at 500m AGL is originated from Turkmenistan and is transported through
Afghanistan. The air parcels at 1000m and 1500m AGL are originated from
Afghanistan. The air mass at 1500m AGL seems to carry pollutants from the Kabul
city and there is high level of stagnation at this altitude.
Figure 5.17. Back Trajectory Analysis of High Ozone Episode in Islamabad during June 19-23, 2010
131
Figure 5.18 shows the back trajectory for a high ozone episode in Islamabad occurred
on 22nd-24th April, 2011. Total run time for back trajectory is 72 hours. It is revealed
from the trajectory that the air parcel at 1500m AGL is originated in Kazakhstan and
is transported through Turkmenistan, Uzbekistan, Tajikistan and Afghanistan.
Whereas, the air masses at 500m and 1000m AGL are originated from Tajikistan and
transported to Islamabad through Afghanistan.
Figure 5.18. Back Trajectory Analysis of High Ozone Episode in Islamabad during April 22-24, 2011
132
Figure 5.19 shows the back trajectory for a high ozone episode in Islamabad during
May 16-20, 2011. Total run time for back trajectory is 72 hours. It is revealed from
the back trajectory that the air masses at altitudes 500m, 1000m and 1500m AGL are
all originated from Turkmenistan and are moved towards Islamabad through
Uzbekistan and Tajikistan.
Figure 5.19. Back Trajectory Analysis of High Ozone Episode in Islamabad during May 16-20, 2011
133
Figure 5.20 shows the back trajectory for a high ozone episode in Islamabad during
22nd – 25th May, 2011. Total run time for back trajectory is 96 hours. It is revealed
from the back trajectory that the air masses at altitudes 500m and 1000m AGL are
originated from Indian Kashmir, whereas, the air mass at 1500m AGL is being
transported from Southern India passing through Indian Punjab towards Islamabad.
Figure 5.20. Back Trajectory Analysis of High Ozone Episode in Islamabad during May 22-25, 2011
134
The back trajectory for high ozone episode of June 2-25, 2011 is shown in Figure
5.21. Total run time for the back trajectory is 72 hours. It is observed that the air mass
at 500m AGL is originated from Turkmenistan and the air parcels at 1000m and
1500m AGL have their origin in Afghanistan. These air masses are transported to
Islamabad through West India indicating the pollution sources from India contributing
to high ozone event as well.
Figure 5.21. Back Trajectory Analysis of High Ozone Episode in Islamabad during
June 2-25, 2011
135
Figure 5.22 shows the back trajectory for a high ozone episode in Islamabad during
July 4-6, 2011. The back trajectory for this episode is run for 120 hours in order to
identify the possible pollution sources. It is revealed from the back trajectory that the
air masses prior to the high ozone event are transported from India bringing pollutant.
Figure 5.22. Back Trajectory Analysis of High Ozone Episode in Islamabad during
July 4-6, 2011
136
The back trajectory for high ozone episode of July 10-13, 2011 is shown in Figure
5.23. Total run time for the back trajectory is 96 hours. It is evident from the
trajectory that the emission sources in India are contributing to this high ozone
episode as the direction of air mass transport is from East.
Figure 5.23. Back Trajectory Analysis of High Ozone Episode in Islamabad during July 10-13, 2011
137
5.3. Weather Research and Forecasting (WRF) Model Simulations
Two high ozone episodes have been selected for simulation by Weather
Research and Forecasting (WRF) model. The first selected episode occurred during
June 9-15, 2009 and the second high ozone event was observed during August 15-19,
2011. The synoptic analysis has been conducted for meteorological conditions at
850hPa and 950hPa in order to appropriately assess the role of meteorology in high
level of ozone during selected durations for Islamabad city.
5.3.1. High Ozone Episode during June 9-15, 2009
The detailed synoptic analysis for each day of the episode is given as follows:
June 09, 2009
WRF Simulation has shown that at 850hPa, warm dry northerly wind flow is
quite significant over the study area at daytime. Temperature profile at the same level
ranges from 25 to 30oC. The synoptic simulation shows that there is almost stagnation
over Islamabad Northerly winds ranging between 6-8 m s-1 are flowing across eastern
and western side of the study area. At 925hPa, north-westerly winds are advecting
into Islamabad with a speed of 6 m s-1.
At night time, two wind components i.e., north-westerly dry winds and south-
westerly moist winds are advecting into the city at 850hPa. These two wind
components are converging in the study area due to mountain barriers in Islamabad.
Wind speed is observed to be 2 m s-1 which shows stagnation in the area. At night
time, south-westerly winds are transported by advection in Islamabad. The night time
temperature at 925hPa ranges 35-40oC.
138
Figure 5.24(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009
Figure 5.24(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 09, 2009
139
Figure 5.24(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009
Figure 5.24(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 09, 2009
140
June 10, 2009
At daytime, the temperature ranges between 30 to 35oC at both 850hPa and
925hPa levels. Wind pattern has also been observed similar for both pressure levels
i.e., south-easterly winds are advecting into the study area with the wind speed of 2 m
s-1.
At night time, the temperature of Islamabad at both pressure levels is 30-35oC.
South-westerly and north-easterly wind components are dominant at 850hPa with a
wind speed of 2 m s-1 which implies the stagnant conditions in the city. At pressure
level 925hPa, only north-easterly wind component is dominant in the area with very
high wind speed of 10 m s-1.
June 11, 2009
The daytime temperature of Islamabad ranged between 30o to 35oC at 850hPa.
Wind vectors show that there is advection of south-easterly winds with very low wind
speed of 2 m s-1. Such low wind speed shows that there is stagnation in the area. At
925hPa, south-easterly and north-westerly winds components are converging in the
area. However, the wind speed is quite low i.e., 2 m s-1.
At night time, north-easterly winds are advecting in the city with a wind speed
of about 6 m s-1 and temperature range of 30o to 35o C at the pressure level of 850hPa.
At 925hPa, the temperature ranges between 25o to 30oC. There is advection of
easterly winds in the study area with a wind speed of 10 m s-1.
141
Figure 5.25(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 10, 2009
Figure 5.25(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 10, 2009
142
Figure 5.25(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 10, 2009
Figure 5.25(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 10, 2009
143
Figure 5.26(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 11, 2009
Figure 5.26(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 11, 2009
144
Figure 5.26(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 11, 2009
Figure 5.26(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 11, 2009
145
June 12, 2009
Daytime temperature ranges 30o-35oC in Islamabad on June 12, 2009 for both
pressure levels. At 850hPa, north-westerly and northerly wind components advecting
into the city but there is no convergence. Wind vectors are widely spaced which
means that there is stagnation in the area. At 925hPa, only north-westerly winds are
advected into Islamabad with a wind speed of 4 m s-1.
At night time, north-westerly winds are advecting in Islamabad with a wind
speed of about 4 m s-1 and temperature ranging 30o-35o C at 850hPa. The night time
temperature at 925hPa is 35o-40oC. North-westerly and northerly winds components
are advecting into the city with a wind speed of about 8 m s-1.
June 13, 2009
On June 13, 2009, daytime temperature ranges 30o-35oC at both the pressure
levels. South-easterly wind component is dominantly advecting in Islamabad at
850hPa and 925hPa. The wind speed is about 2 m s-1 implying stagnation in the area.
At night time, northerly winds are advecting into the city with a high wind
speed of 14 m s-1 leading to dispersion at 850hPa. At the pressure level of 925hPa,
two winds components i.e., northerly and north-easterly winds are advecting with
convergence in the study area. The wind speed at this level is 8 m s-1.
146
Figure 5.27(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 12, 2009
Figure 5.27(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 12, 2009
147
Figure 5.27(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 12, 2009
Figure 5.27(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 12, 2009
148
Figure 5.28(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 13, 2009
Figure 5.28(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 13, 2009
149
Figure 5.28(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 13, 2009
Figure 5.28 (d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed
(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 13, 2009
150
June 14, 2009
Daytime temperature on June 14, 2009 at 850hPa ranges 30o-35oC at both the
pressure levels. Northerly and southerly wind components are being converged in
Islamabad at both the pressure levels with a wind speed of 10 m s-1.
At night time, easterly winds are advecting in the study area with a speed of 10
m s-1 at both the pressure levels.
June 15, 2009
At daytime, south-easterly winds are advecting in the city at the pressure
levels 850hPa and 925hPa. Wind speed at 850hPa is 6 m s-1, however, it is 8 m s-1at
925hPa. temperature on June 14, 2009 at 850hPa ranges 30o-35oC at both the pressure
levels. Northerly and southerly wind components are being converged in Islamabad at
both the pressure levels with a wind speed of 10 m s-1.
At night time, south-easterly winds are advecting in the study area at 850hPa
and easterly winds are transported to the study area at 925hPa. Wind speed at both the
pressure levels is 10 m s-1 which is quite high.
151
Figure 5.29(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 14, 2009
Figure 5.29(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 14, 2009
152
Figure 5.29(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 14, 2009
Figure 5.29(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 14, 2009
153
Figure 5.30(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 15, 2009
Figure 5.30(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 15, 2009
154
Figure 5.30(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 15, 2009
Figure 5.30(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 15, 2009
155
5.3.2. High Ozone Episode during August 15-19, 2011
Another high ozone episode during August 15-19, 2011 was selected for
simulation by WRF model in order to do synoptic analysis so that a link between air
pollution and meteorology may be assessed. The synoptic analysis is given below:
August 15, 2011
On August 15, 2011, south-westerly winds are dominant at both the pressure
levels during daytime. The wind speed at 925hPa is about 4 m s-1 whereas; there is
stagnation at 850hPa.
At 850hPa, south westerly winds are advecting in the study area and the wind
speed is about 4 m s-1 during night-time. However, southerly and south-easterly wind
components are dominant in this area at the pressure level of 925hPa. The wind speed
at this level is about 8 m s-.
August 16, 2011
During daytime of August 16, 2011, the wind pattern is same for two pressure
levels with south-westerly winds and a speed of 2 m s-1. The atmosphere remained
quite stagnant during this time. During night-time, south-easterly winds are dominant
in the area at 850hPa and 925hPa. The wind speed is about 4 m s-1 at 850hPa and 6 m
s-1 at 925hPa.
156
Figure 5.31(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011
Figure 5.31(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 15, 2011
157
Figure 5.31(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011
Figure 5.31(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 15, 2011
158
Figure 5.32(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 16, 2011
Figure 5.32(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 16, 2011
159
Figure 5.32(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 16, 2011
Figure 5.32(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 16, 2011
160
August 17, 2011
On August 17, 2011, south-easterly winds are dominant at both the pressure
levels during day and night-time. There is stagnation in the area with wind speed of
about 2 m s-1.
August 18, 2011
On August 18, 2011, stagnant conditions are prevailing at 850hPa during day
and night. At pressure level of 925hPa, there is stagnation during daytime, however,
south-easterly wind component is advecting in the study area during night time with a
wind speed of 4 m s-1.
August 19, 2011
During daytime of August 19, 2011, diminished south-easterly winds upto 2 m
s-1 are passing through the study area at 850hPa. Overall condition is stagnant. At
925hPa, south-easterly winds are advecting with stagnant conditions.
At night-time, there is advection of south-easterly winds in the area with a
speed of 4 m s-1 at 850hPa and 2 m s-1 at 925hPa.
161
Figure 5.33(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 17, 2011
Figure 5.33(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 17, 2011
162
Figure 5.33(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 17, 2011
Figure 5.33(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 17, 2011
163
Figure 5.34(a). Daytime Averaged Air Temperature (oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 18, 2011
Figure 5.34(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 18, 2011
164
Figure 5.34(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 18, 2011
Figure 5.34(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 18, 2011
165
Figure 5.35(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 19, 2011
Figure 5.35(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 19, 2011
166
Figure 5.35(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 19, 2011
Figure 5.35(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 19, 2011
167
5.4. Conclusions
The HYSPLIT back trajectories have revealed that the transbounadry
predominantly back trajectories are originated from Afghanistan, Iran and India. The
most frequent path of trajectories is west, east and south. Furthermore, simulations of
two selected high ozone episodes were carried out by using Weather Research and
Forecasting (WRF) model to assess the influence of meteorological conditions on
level and variation of ozone during episode period. High ozone concentrations have
been observed during warm dry conditions. High ozone concentrations have also been
observed during precipitation days due to intrusion of transboundary pollution
dominating the air quality. The synoptic analysis through WRF simulations has given
an insight into the meteorological condition. It has been revealed that most of the
episodes have occurred during stagnant conditions implying the role of accumulation
of pollutants towards poor air quality of Islamabad. Advection of air masses from
south-east and south-west are also playing a major role in elevating the pollution level
of the city.
168
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APPENDICES
Appendix A
Research Publications
• Rasheed, A., Aneja, V.P., and Aiyyer, A., Rafique, U., Measurements and analysis of air quality in Islamabad, Pakistan', AGU Earth’s Future, ISSN: 2328-4277, DOI: 10.1002/2013EF000174 (In press).
• Rasheed, A., Aneja, V.P., Aiyyer, A., and Rafique, U., ‘Measurement and analysis of fine particulate matter (PM2.5) in urban areas of Pakistan' Aerosols and Air Quality Research IF-2.827 (submitted).
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Appendix B
Conference Presentations
• Presentation on 'Ambient Air Quality Analysis in Urban Areas of Pakistan’ at the International Conference held on April 9-10, 2014 at Shah Abdul Latif University, Khairpur Mir’s, Pakistan.
• Presentation on 'Ambient Air Quality of Islamabad-A Monitoring Based Analysis' at the International Workshop on ‘Atmospheric Composition and the Asian Monsoon (ACAM)’ held on June 9-12, 2013, Kathmandu, Nepal.
• Presentation on 'Ambient Air Quality of Islamabad-A Monitoring Based Analysis' at the international workshop on 'Changing Chemistry in Changing Climate: Monsoon-2013' held on May 1-3, 2013 at Indian Institute of Tropical Meteorology, Pune, India.
• Presentation of research paper entitled 'Characterization of Fine Particulate Matter (PMfine) in Urban Areas of Pakistan-An Observational Based Analysis' in session 'Atmospheric Chemistry: Gas-Particle Interactions and Climate Change - II' in The Southeastern Regional Meeting of the American Chemical Society held on November 14-17, 2012, Raleigh, N.C., U.S.A.
• Presentation on 'Characterization of Fine Particulate Matter (PMfine) in Urban Areas of Pakistan' on PAMS Access Day on September 6, 2012 organized by the College of Physical and Mathematical Sciences, North Carolina State University, Raleigh N.C., U.S.A.
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